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<section id="module-wonambi.detect.agreement">
<span id="wonambi-detect-agreement-module"></span><h1>wonambi.detect.agreement module<a class="headerlink" href="#module-wonambi.detect.agreement" title="Permalink to this heading"></a></h1>
<p>Module for agreement and consensus analysis between raters</p>
<dl class="py class">
<dt class="sig sig-object py" id="wonambi.detect.agreement.MatchedEvents">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">wonambi.detect.agreement.</span></span><span class="sig-name descname"><span class="pre">MatchedEvents</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detection</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/wonambi/detect/agreement.html#MatchedEvents"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#wonambi.detect.agreement.MatchedEvents" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Class for storing matched events and producing statistics.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tp</strong> (<em>ndarray</em>) – true positives as boolean array of shape len(detection) x len(standard)</p></li>
<li><p><strong>fp</strong> (<em>ndarray</em>) – indices of false positives in detection</p></li>
<li><p><strong>fn</strong> (<em>ndarray</em>) – indices of false negatives in standard</p></li>
<li><p><strong>detection</strong> (<em>list</em><em> of </em><em>dict</em>) – list of detected events tested against the standard, with ‘start’,
‘end’ and ‘chan’</p></li>
<li><p><strong>standard</strong> (<em>list</em><em> of </em><em>dict</em>) – list of ground-truth events, with ‘start’, ‘end’ and ‘chan’</p></li>
<li><p><strong>threshold</strong> (<em>float</em>) – minimum intersection-union score for events to be considered
overlapping</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="wonambi.detect.agreement.MatchedEvents.all_to_annot">
<span class="sig-name descname"><span class="pre">all_to_annot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">annot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['TPd',</span> <span class="pre">'TPs',</span> <span class="pre">'FP',</span> <span class="pre">'FN']</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/wonambi/detect/agreement.html#MatchedEvents.all_to_annot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#wonambi.detect.agreement.MatchedEvents.all_to_annot" title="Permalink to this definition"></a></dt>
<dd><p>Convenience function to write all events to XML by category, showing
overlapping TP detection and TP standard.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="wonambi.detect.agreement.MatchedEvents.f1score">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">f1score</span></span><a class="headerlink" href="#wonambi.detect.agreement.MatchedEvents.f1score" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="wonambi.detect.agreement.MatchedEvents.precision">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">precision</span></span><a class="headerlink" href="#wonambi.detect.agreement.MatchedEvents.precision" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="wonambi.detect.agreement.MatchedEvents.recall">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">recall</span></span><a class="headerlink" href="#wonambi.detect.agreement.MatchedEvents.recall" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="wonambi.detect.agreement.MatchedEvents.to_annot">
<span class="sig-name descname"><span class="pre">to_annot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">annot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">category</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/wonambi/detect/agreement.html#MatchedEvents.to_annot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#wonambi.detect.agreement.MatchedEvents.to_annot" title="Permalink to this definition"></a></dt>
<dd><p>Write matched events to Wonambi XML file for visualization.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>annot</strong> (<em>instance</em><em> of </em><a class="reference internal" href="wonambi.attr.annotations.html#wonambi.attr.annotations.Annotations" title="wonambi.attr.annotations.Annotations"><em>Annotations</em></a>) – Annotations file</p></li>
<li><p><strong>category</strong> (<em>str</em>) – ‘tp_cons’, ‘tp_det’, ‘tp_std’, ‘fp’ or ‘fn’</p></li>
<li><p><strong>name</strong> (<em>str</em>) – name for the event type</p></li>
<li><p><strong>s_freq</strong> (<em>int</em>) – sampling frequency, in Hz, only required for ‘tp_cons’ category</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="wonambi.detect.agreement.consensus">
<span class="sig-prename descclassname"><span class="pre">wonambi.detect.agreement.</span></span><span class="sig-name descname"><span class="pre">consensus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">events</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_duration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/wonambi/detect/agreement.html#consensus"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#wonambi.detect.agreement.consensus" title="Permalink to this definition"></a></dt>
<dd><p>Take two or more event lists and output a merged list based on
consensus.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>events</strong> (<em>tuple</em><em> of </em><em>lists</em><em> of </em><em>dict</em>) – two or more lists of events from different raters, with ‘start’, ‘end’
and ‘chan’</p></li>
<li><p><strong>threshold</strong> (<em>float</em>) – value between 0 and 1 to threshold consensus. Consensus is computed on
a per-sample basis. For a given rater, if an event is present at a
sample, that rater-sample is assigned the value 1; otherwise it is
assigned 0. The arithmetic mean is taken per sample across all raters,
and if this mean exceeds ‘threshold’, the sample is counted as
belonging to a merged event.</p></li>
<li><p><strong>s_freq</strong> (<em>int</em>) – sampling frequency, in Hz</p></li>
<li><p><strong>min_duration</strong> (<em>float</em><em>, </em><em>optional</em>) – minimum duration for merged events, in s.</p></li>
<li><p><strong>weights</strong> (<em>list</em><em> of </em><em>float</em>) – a vector of relative weights of each event type</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><em>instance of wonambi.Graphoelement</em> – events merged by consensus</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="wonambi.detect.agreement.consensus_exact">
<span class="sig-prename descclassname"><span class="pre">wonambi.detect.agreement.</span></span><span class="sig-name descname"><span class="pre">consensus_exact</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">events</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_duration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/wonambi/detect/agreement.html#consensus_exact"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#wonambi.detect.agreement.consensus_exact" title="Permalink to this definition"></a></dt>
<dd><p>Take two or more event lists and output a merged list based on
consensus, where agreement is exactly equal to a threshold.
This is useful when combining >2 event types, and creating a
consensus event type based on some combination of these events.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>events</strong> (<em>tuple</em><em> of </em><em>lists</em><em> of </em><em>dict</em>) – two or more lists of events from different raters, with ‘start’, ‘end’
and ‘chan’</p></li>
<li><p><strong>threshold</strong> (<em>float</em>) – value between 0 and 1 to threshold consensus. Consensus is computed on
a per-sample basis. For a given rater, if an event is present at a
sample, that rater-sample is assigned the value 1; otherwise it is
assigned 0. The arithmetic sum is taken per sample across all raters,
and if this exactly equals ‘threshold’, the sample is counted as
belonging to a merged event.</p></li>
<li><p><strong>s_freq</strong> (<em>int</em>) – sampling frequency, in Hz</p></li>
<li><p><strong>min_duration</strong> (<em>float</em><em>, </em><em>optional</em>) – minimum duration for merged events, in s.</p></li>
<li><p><strong>weights</strong> (<em>a dict containing event names</em><em> (</em><em>str</em><em>) </em><em>and their corresponding</em>) – weighting (int) e.g. {‘low’ : 1,’med’ : 2,’high’ : 3}</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><em>instance of wonambi.Graphoelement</em> – events merged by consensus and named by confidence rating</p>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>This function is a modification of agreement.consensus contributed by
Nathan Cross.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="wonambi.detect.agreement.match_events">
<span class="sig-prename descclassname"><span class="pre">wonambi.detect.agreement.</span></span><span class="sig-name descname"><span class="pre">match_events</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">detection</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standard</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/wonambi/detect/agreement.html#match_events"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#wonambi.detect.agreement.match_events" title="Permalink to this definition"></a></dt>
<dd><p>Find best matches between detected and standard events, by a thresholded
intersection-union rule.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>detection</strong> (<em>list</em><em> of </em><em>dict</em>) – list of detected events to be tested against the standard, with
‘start’, ‘end’ and ‘chan’</p></li>
<li><p><strong>standard</strong> (<em>list</em><em> of </em><em>dict</em>) – list of ground-truth events, with ‘start’, ‘end’ and ‘chan’</p></li>
<li><p><strong>threshold</strong> (<em>float</em>) – minimum intersection-union score to match a pair, between 0 and 1</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><em>instance of MatchedEvents</em> – indices of true positives, false positives and false negatives, with
statistics (recall, precision, F1)</p>
</dd>
</dl>
</dd></dl>
</section>
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