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(v0.8.0.9033) antivirals data set, cleanup
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@ -41,7 +41,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.8.0.9032</span>
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</span>
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</div>
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@ -187,7 +187,7 @@
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<h1>Benchmarks</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">16 October 2019</h4>
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<h4 class="date">18 November 2019</h4>
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<div class="hidden name"><code>benchmarks.Rmd</code></div>
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@ -221,21 +221,21 @@
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<a class="sourceLine" id="cb2-16" data-line-number="16"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
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<a class="sourceLine" id="cb2-17" data-line-number="17"><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>)</a>
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<a class="sourceLine" id="cb2-18" data-line-number="18"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 9.9 10 14 11 12 35 10</span></a>
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<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 33.0 33 39 38 39 52 10</span></a>
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<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 32.0 36 44 38 56 68 10</span></a>
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<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 10.0 10 13 11 11 34 10</span></a>
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<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 10.0 11 19 12 33 42 10</span></a>
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<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 25.0 26 32 28 32 53 10</span></a>
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<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 24.0 25 31 27 30 52 10</span></a>
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<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 31.0 32 39 34 38 84 10</span></a>
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<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 610.0 640 680 680 710 770 10</span></a>
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<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 330.0 340 350 350 360 370 10</span></a>
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<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 9.8 10 13 11 12 34 10</span></a>
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<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 20.0 22 29 24 31 57 10</span></a>
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<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 20.0 20 29 23 42 47 10</span></a>
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<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 21.0 23 30 25 43 47 10</span></a></code></pre></div>
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<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 10 10 16 11 13 58 10</span></a>
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<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 33 34 50 45 67 80 10</span></a>
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<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 34 38 44 39 43 72 10</span></a>
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<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 10 11 16 12 15 44 10</span></a>
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<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 11 11 17 12 14 59 10</span></a>
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<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 26 30 37 33 48 54 10</span></a>
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<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 26 27 31 28 32 48 10</span></a>
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<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 31 35 37 37 40 44 10</span></a>
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<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 650 690 750 710 840 900 10</span></a>
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<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 380 410 430 430 440 500 10</span></a>
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<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 10 11 20 11 37 44 10</span></a>
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<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 20 22 51 29 47 220 10</span></a>
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<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 21 23 32 25 41 57 10</span></a>
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<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 20 21 23 22 23 27 10</span></a></code></pre></div>
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<p><img src="benchmarks_files/figure-html/unnamed-chunk-5-1.png" width="562.5"></p>
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<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>
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<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>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
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@ -247,19 +247,19 @@
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<a class="sourceLine" id="cb3-6" data-line-number="6"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
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<a class="sourceLine" id="cb3-7" data-line-number="7"><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>)</a>
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<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq</span></a>
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<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("metsem") 1343.00 1385.00 1398.00 1403.00 1418.0</span></a>
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<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("METSEM") 1299.00 1356.00 1397.00 1396.00 1442.0</span></a>
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<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("M. semesiae") 1892.00 2028.00 2052.00 2041.00 2084.0</span></a>
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<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("M. semesiae") 1990.00 2017.00 2062.00 2032.00 2094.0</span></a>
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<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Methanosarcina semesiae") 32.63 33.24 38.58 35.82 40.2</span></a>
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<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq</span></a>
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<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("metsem") 1415.00 1467.00 1505.00 1493.00 1537.00</span></a>
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<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("METSEM") 1447.00 1488.00 1530.00 1521.00 1555.00</span></a>
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<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("M. semesiae") 2216.00 2239.00 2293.00 2284.00 2351.00</span></a>
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<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("M. semesiae") 2143.00 2212.00 2304.00 2307.00 2332.00</span></a>
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<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Methanosarcina semesiae") 32.44 35.59 43.59 38.19 45.88</span></a>
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<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co"># max neval</span></a>
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<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1437.00 10</span></a>
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<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 1488.00 10</span></a>
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<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 2169.00 10</span></a>
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<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 2245.00 10</span></a>
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<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 57.04 10</span></a></code></pre></div>
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<p>That takes 14.3 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>Methanosarcina semesiae</em>) are almost fast - these are the most probable input from most data sets.</p>
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<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1627.00 10</span></a>
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<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 1628.00 10</span></a>
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<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 2395.00 10</span></a>
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<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 2535.00 10</span></a>
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<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 69.48 10</span></a></code></pre></div>
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<p>That takes 13.8 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>Methanosarcina semesiae</em>) are almost fast - these are the most probable input from most data sets.</p>
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<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>
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<p><img src="benchmarks_files/figure-html/unnamed-chunk-9-1.png" width="562.5"></p>
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<p>In reality, the <code><a href="../reference/as.mo.html">as.mo()</a></code> functions <strong>learns from its own output to speed up determinations for next times</strong>. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use <code><a href="../reference/as.mo.html">as.mo()</a></code> yourself:</p>
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@ -296,8 +296,8 @@
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<a class="sourceLine" id="cb4-24" data-line-number="24"><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>)</a>
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<a class="sourceLine" id="cb4-25" data-line-number="25"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb4-26" data-line-number="26"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 645 661 683 672 686 771 10</span></a></code></pre></div>
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<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.67 seconds (671 ms). You only lose time on your unique input values.</p>
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<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 642 674 702 687 713 843 10</span></a></code></pre></div>
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<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.69 seconds (686 ms). You only lose time on your unique input values.</p>
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</div>
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<div id="precalculated-results" class="section level3">
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<h3 class="hasAnchor">
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@ -309,10 +309,10 @@
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<a class="sourceLine" id="cb5-4" data-line-number="4"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
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<a class="sourceLine" id="cb5-5" data-line-number="5"><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>)</a>
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<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.380 6.530 7.750 7.180 8.21 11.30 10</span></a>
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<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 24.300 25.500 30.400 26.900 31.60 54.80 10</span></a>
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<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.803 0.827 0.926 0.869 0.95 1.22 10</span></a></code></pre></div>
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<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.350 6.420 6.880 6.530 7.180 8.56 10</span></a>
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<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 24.600 25.100 31.300 25.700 27.900 62.80 10</span></a>
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<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.798 0.867 0.914 0.892 0.931 1.14 10</span></a></code></pre></div>
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<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>
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<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1">run_it <-<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>),</a>
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<a class="sourceLine" id="cb6-2" 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>
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@ -326,14 +326,14 @@
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<a class="sourceLine" id="cb6-10" data-line-number="10"><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>)</a>
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<a class="sourceLine" id="cb6-11" data-line-number="11"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb6-12" data-line-number="12"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.509 0.530 0.681 0.565 0.661 1.630 10</span></a>
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<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.518 0.526 0.598 0.553 0.564 0.875 10</span></a>
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<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.848 0.882 1.100 0.990 1.180 1.920 10</span></a>
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<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.566 0.592 0.734 0.714 0.765 1.120 10</span></a>
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<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.486 0.522 0.555 0.542 0.551 0.681 10</span></a>
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<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.466 0.493 0.598 0.553 0.586 1.110 10</span></a>
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<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.462 0.498 0.598 0.525 0.671 0.921 10</span></a>
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<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.480 0.489 0.566 0.508 0.628 0.756 10</span></a></code></pre></div>
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<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.446 0.459 0.471 0.475 0.481 0.485 10</span></a>
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<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.502 0.506 0.518 0.513 0.521 0.568 10</span></a>
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<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.774 0.802 0.811 0.815 0.823 0.838 10</span></a>
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<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.505 0.511 0.532 0.515 0.534 0.627 10</span></a>
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<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.467 0.469 0.495 0.478 0.481 0.676 10</span></a>
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<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.457 0.467 0.478 0.479 0.484 0.509 10</span></a>
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<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.452 0.460 0.467 0.471 0.472 0.478 10</span></a>
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<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.460 0.469 0.473 0.473 0.480 0.486 10</span></a></code></pre></div>
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<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>
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</div>
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<div id="results-in-other-languages" class="section level3">
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@ -359,14 +359,14 @@
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<a class="sourceLine" id="cb7-17" data-line-number="17"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
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<a class="sourceLine" id="cb7-18" data-line-number="18"><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>)</a>
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<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 20.01 20.76 28.07 22.47 29.04 54.04 10</span></a>
|
||||
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 21.95 22.28 26.91 22.72 24.24 58.36 10</span></a>
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||||
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 27.57 27.96 47.86 31.50 54.01 149.80 10</span></a>
|
||||
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 22.08 22.13 28.59 24.11 26.45 58.61 10</span></a>
|
||||
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 21.73 22.33 25.39 25.30 26.90 29.57 10</span></a>
|
||||
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 22.23 22.98 24.95 23.45 23.70 34.08 10</span></a>
|
||||
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 22.02 23.03 24.80 24.37 27.14 28.33 10</span></a></code></pre></div>
|
||||
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 20.49 21.64 27.36 23.44 27.96 53.91 10</span></a>
|
||||
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 23.26 23.37 28.22 24.42 26.13 60.60 10</span></a>
|
||||
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 28.62 29.74 36.90 30.18 33.12 72.41 10</span></a>
|
||||
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 21.77 23.08 27.71 24.75 26.98 55.14 10</span></a>
|
||||
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 22.54 23.37 25.19 25.25 26.41 29.08 10</span></a>
|
||||
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 23.04 23.99 30.97 26.14 30.78 52.61 10</span></a>
|
||||
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 22.40 23.07 24.26 23.14 24.44 31.60 10</span></a></code></pre></div>
|
||||
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Reference in New Issue
Block a user