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<span class="target" id="module-nussl.evaluation"></span><div class="section" id="evaluation">
<h1>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this headline">¶</a></h1>
<div class="section" id="evaluation-base">
<h2>Evaluation base<a class="headerlink" href="#evaluation-base" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="nussl.evaluation.EvaluationBase">
<em class="property">class </em><code class="sig-prename descclassname">nussl.evaluation.</code><code class="sig-name descname">EvaluationBase</code><span class="sig-paren">(</span><em class="sig-param">true_sources_list</em>, <em class="sig-param">estimated_sources_list</em>, <em class="sig-param">source_labels=None</em>, <em class="sig-param">compute_permutation=False</em>, <em class="sig-param">best_permutation_key=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/evaluation_base.html#EvaluationBase"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.EvaluationBase" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for all Evaluation classes for source separation algorithms in nussl.
Contains common functions for all evaluation techniques. This class should not be
instantiated directly.</p>
<p>Both <code class="docutils literal notranslate"><span class="pre">true_sources_list</span></code> and <code class="docutils literal notranslate"><span class="pre">estimated_sources_list</span></code> get validated
using the private method <code class="xref py py-func docutils literal notranslate"><span class="pre">_verify_input_list()</span></code>. If your evaluation
needs to verify that input is set correctly (recommended) overwrite that method
to add checking.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>true_sources_list</strong> (<em>list</em>) – List of objects that contain one ground truth source per object.
In some instances (such as the <code class="xref py py-class docutils literal notranslate"><span class="pre">BSSEval</span></code> objects) this list is filled with
<code class="xref py py-class docutils literal notranslate"><span class="pre">AudioSignals</span></code> but in other cases it is populated with
<code class="xref py py-class docutils literal notranslate"><span class="pre">MaskBase</span></code> -derived objects (i.e., either a <code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryMask</span></code> or
<code class="xref py py-class docutils literal notranslate"><span class="pre">SoftMask</span></code> object).</p></li>
<li><p><strong>estimated_sources_list</strong> (<em>list</em>) – List of objects that contain source estimations from a source
separation algorithm. List should be populated with the same type of objects and in the
same order as <a href="#id1"><span class="problematic" id="id2">:param:`true_sources_list`</span></a>.</p></li>
<li><p><strong>source_labels</strong> (<em>list</em>) – List of strings that are labels for each source to be used as keys for
the scores. Default value is <cite>None</cite> and in that case labels use the file_name attribute.
If that is also <cite>None</cite>, then the source labels are <cite>Source 0</cite>, <cite>Source 1</cite>, etc.</p></li>
<li><p><strong>compute_permutation</strong> (<em>bool</em>) – Whether or not to evaluate in a permutation-invariant
fashion, where the estimates are permuted to match the true sources. Only the
best permutation according to <code class="docutils literal notranslate"><span class="pre">best_permutation_key</span></code> is returned to the
scores dict. Defaults to False.</p></li>
<li><p><strong>best_permutation_key</strong> (<em>str</em>) – Which metric to use to decide which permutation of
the sources was best.</p></li>
<li><p><strong>**kwargs</strong> (<em>dict</em>) – Any additional keyword arguments are passed on to <code class="docutils literal notranslate"><span class="pre">evaluate_helper</span></code>.</p></li>
</ul>
</dd>
</dl>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#nussl.evaluation.EvaluationBase.evaluate" title="nussl.evaluation.EvaluationBase.evaluate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">evaluate</span></code></a>()</p></td>
<td><p>This function encapsulates the main functionality of all evaluation classes.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#nussl.evaluation.EvaluationBase.evaluate_helper" title="nussl.evaluation.EvaluationBase.evaluate_helper"><code class="xref py py-obj docutils literal notranslate"><span class="pre">evaluate_helper</span></code></a>(references, estimates, **kwargs)</p></td>
<td><p>This function should be implemented by each class that inherits this class.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#nussl.evaluation.EvaluationBase.get_candidates" title="nussl.evaluation.EvaluationBase.get_candidates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_candidates</span></code></a>()</p></td>
<td><p>This gets all the possible candidates for evaluation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#nussl.evaluation.EvaluationBase.preprocess" title="nussl.evaluation.EvaluationBase.preprocess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code></a>()</p></td>
<td><p>Takes the objects contained in <cite>true_sources_list</cite> and <cite>estimated_sources_list</cite> and processes them into numpy arrays that have shape (…, n_channels, n_sources).</p></td>
</tr>
</tbody>
</table>
<p><strong>Attributes</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#nussl.evaluation.EvaluationBase.scores" title="nussl.evaluation.EvaluationBase.scores"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scores</span></code></a></p></td>
<td><p>A dictionary that stores all scores from the evaluation method.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="nussl.evaluation.EvaluationBase.evaluate">
<code class="sig-name descname">evaluate</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/evaluation_base.html#EvaluationBase.evaluate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.EvaluationBase.evaluate" title="Permalink to this definition">¶</a></dt>
<dd><p>This function encapsulates the main functionality of all evaluation classes.
It performs the following steps, some of which must be implemented in subclasses
of EvaluationBase.</p>
<blockquote>
<div><ol class="arabic simple">
<li><p>Preprocesses the data somehow into numpy arrays that get passed into your
evaluation function.</p></li>
<li><p>Gets all possible candidates that will be evaluated in your evaluation function.</p></li>
<li><p>For each candidate, runs the evaluation function (must be implemented in subclass).</p></li>
<li><p>Finds the results from the best candidate.</p></li>
<li><p>Returns a dictionary containing those results.</p></li>
</ol>
</div></blockquote>
<p>Steps 1 and 3 must be implemented by the subclass while the others are implemented
by EvaluationBase.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A dictionary containing the scores for each source for the best candidate.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="nussl.evaluation.EvaluationBase.evaluate_helper">
<code class="sig-name descname">evaluate_helper</code><span class="sig-paren">(</span><em class="sig-param">references</em>, <em class="sig-param">estimates</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/evaluation_base.html#EvaluationBase.evaluate_helper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.EvaluationBase.evaluate_helper" title="Permalink to this definition">¶</a></dt>
<dd><p>This function should be implemented by each class that inherits this
class. The function should take in a numpy array containing the references and
one for the estimates and compute evaluation measures between the two arrays.
The results should be stored in a list of dictionaries. For example, a
BSSEval evaluator may return a dictionary as follows, for a single estimate:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span> #or windows or both
[ { #ch0 ch1 # results for first estimate
"SDR": [5.6, 5.2], # metric
"SIR": [9.2, 8.9], # metric
"SAR": [4.1, 4.3] # metric
},
... # more results for other estimates
]
</pre></div>
</div>
<p>Each metric should be a key in the dictionary pointing to a value which is a
list. The list will contain the metrics for however the algorithm was implemented
(e.g. there might be two value, one for each channel in a stereo mix, or there
might be a sequence, one for each window that was evaluated.)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>references</strong> (<em>np.ndarray</em>) – References kept in a numpy array. Should have shape
(…, n_channels, n_sources).</p></li>
<li><p><strong>estimates</strong> (<em>np.ndarray</em>) – Estimates kept in whatever format you want. Should have
shape (…, n_channels, n_sources).</p></li>
<li><p><strong>kwargs</strong> (<em>dict</em>) – Keyword arguments with any additional arguments to be used in
the function (e.g. window_size, hop_length).</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of dictionary containing the measures corresponding to each estimate
and reference.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="nussl.evaluation.EvaluationBase.get_candidates">
<code class="sig-name descname">get_candidates</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/evaluation_base.html#EvaluationBase.get_candidates"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.EvaluationBase.get_candidates" title="Permalink to this definition">¶</a></dt>
<dd><p>This gets all the possible candidates for evaluation. If <cite>compute_permutation</cite>
is False, then the estimates and the references are assumed to be in the same
order. The first N estimates will be compared to the first N references, where
N is min(len(estimates), len(references)).</p>
<p>If <cite>compute_permutation</cite> is True, and <cite>len(estimates) == len(references)</cite>, then
every possible ordering of the estimates will be tried to match to the references.
So if there are 3 references and 3 estimates, a total of 3! = 6 candidates will
be generated.</p>
<p>If <cite>compute_permutation</cite> is True and <cite>len(estimates) > len(references)</cite>, then
every combination of size <cite>len(references)</cite> estimates will be tried as well
as their permutations. If there are 2 references and 4 estimates, then
(4 choose 2) = 6 combos will be tried. For each of those pairs of 2, there will
be 2! = 2 permutations. So a total of 12 candidates will be generated.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Two lists of combinations and permutations that should be tried. Each element
of the list contains the indices that are used to find the sources that
are compared to each other.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="nussl.evaluation.EvaluationBase.preprocess">
<code class="sig-name descname">preprocess</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/evaluation_base.html#EvaluationBase.preprocess"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.EvaluationBase.preprocess" title="Permalink to this definition">¶</a></dt>
<dd><p>Takes the objects contained in <cite>true_sources_list</cite> and <cite>estimated_sources_list</cite>
and processes them into numpy arrays that have shape
(…, n_channels, n_sources).</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>references, estimates in that order as np.ndarrays.</p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Make sure to return the preprocessed data in the order
(references, estimates)!</p>
</div>
</dd></dl>
<dl class="method">
<dt id="nussl.evaluation.EvaluationBase.scores">
<em class="property">property </em><code class="sig-name descname">scores</code><a class="headerlink" href="#nussl.evaluation.EvaluationBase.scores" title="Permalink to this definition">¶</a></dt>
<dd><p>A dictionary that stores all scores from the evaluation method. Gets populated when
<a class="reference internal" href="#nussl.evaluation.EvaluationBase.evaluate" title="nussl.evaluation.EvaluationBase.evaluate"><code class="xref py py-func docutils literal notranslate"><span class="pre">evaluate()</span></code></a> gets run.</p>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="bss-evaluation-base">
<h2>BSS Evaluation base<a class="headerlink" href="#bss-evaluation-base" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="nussl.evaluation.BSSEvaluationBase">
<em class="property">class </em><code class="sig-prename descclassname">nussl.evaluation.</code><code class="sig-name descname">BSSEvaluationBase</code><span class="sig-paren">(</span><em class="sig-param">true_sources_list</em>, <em class="sig-param">estimated_sources_list</em>, <em class="sig-param">source_labels=None</em>, <em class="sig-param">compute_permutation=False</em>, <em class="sig-param">best_permutation_key='SDR'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/bss_eval.html#BSSEvaluationBase"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.BSSEvaluationBase" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for all evaluation classes that are based on BSSEval metrics. This
contains some useful verification functions, preprocessing functions that are
used in many separation-based evaluation. Specific evaluation metrics are
thin wrappers around this base class, basically only implementing the
<code class="docutils literal notranslate"><span class="pre">self.evaluate_helper</span></code> function.</p>
<p>Both <code class="docutils literal notranslate"><span class="pre">true_sources_list</span></code> and <code class="docutils literal notranslate"><span class="pre">estimated_sources_list</span></code> get validated
using the private method <code class="xref py py-func docutils literal notranslate"><span class="pre">_verify_input_list()</span></code>. If your evaluation
needs to verify that input is set correctly (recommended) overwrite that method
to add checking.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>true_sources_list</strong> (<em>list</em>) – List of objects that contain one ground truth source per object.
In some instances (such as the <code class="xref py py-class docutils literal notranslate"><span class="pre">BSSEval</span></code> objects) this list is filled with
<code class="xref py py-class docutils literal notranslate"><span class="pre">AudioSignals</span></code> but in other cases it is populated with
<code class="xref py py-class docutils literal notranslate"><span class="pre">MaskBase</span></code> -derived objects (i.e., either a <code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryMask</span></code> or
<code class="xref py py-class docutils literal notranslate"><span class="pre">SoftMask</span></code> object).</p></li>
<li><p><strong>estimated_sources_list</strong> (<em>list</em>) – List of objects that contain source estimations from a source
separation algorithm. List should be populated with the same type of objects and in the
same order as <a href="#id3"><span class="problematic" id="id4">:param:`true_sources_list`</span></a>.</p></li>
<li><p><strong>source_labels</strong> (<em>list</em>) – List of strings that are labels for each source to be used as keys for
the scores. Default value is <cite>None</cite> and in that case labels use the file_name attribute.
If that is also <cite>None</cite>, then the source labels are <cite>Source 0</cite>, <cite>Source 1</cite>, etc.</p></li>
<li><p><strong>compute_permutation</strong> (<em>bool</em>) – Whether or not to evaluate in a permutation-invariant
fashion, where the estimates are permuted to match the true sources. Only the
best permutation according to <code class="docutils literal notranslate"><span class="pre">best_permutation_key</span></code> is returned to the
scores dict. Defaults to False.</p></li>
<li><p><strong>best_permutation_key</strong> (<em>str</em>) – Which metric to use to decide which permutation of
the sources was best.</p></li>
<li><p><strong>**kwargs</strong> (<em>dict</em>) – Any additional arguments are passed on to evaluate_helper.</p></li>
</ul>
</dd>
</dl>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#nussl.evaluation.BSSEvaluationBase.preprocess" title="nussl.evaluation.BSSEvaluationBase.preprocess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code></a>()</p></td>
<td><p>Implements preprocess by stacking the audio_data inside each AudioSignal object in both self.true_sources_list and self.estimated_sources_list.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="nussl.evaluation.BSSEvaluationBase.preprocess">
<code class="sig-name descname">preprocess</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/bss_eval.html#BSSEvaluationBase.preprocess"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.BSSEvaluationBase.preprocess" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements preprocess by stacking the audio_data inside each AudioSignal
object in both self.true_sources_list and self.estimated_sources_list.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Tuple containing reference and estimate arrays.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>tuple</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="scale-invariant-bsseval">
<h2>Scale invariant BSSEval<a class="headerlink" href="#scale-invariant-bsseval" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="nussl.evaluation.BSSEvalScale">
<em class="property">class </em><code class="sig-prename descclassname">nussl.evaluation.</code><code class="sig-name descname">BSSEvalScale</code><span class="sig-paren">(</span><em class="sig-param">true_sources_list</em>, <em class="sig-param">estimated_sources_list</em>, <em class="sig-param">source_labels=None</em>, <em class="sig-param">compute_permutation=False</em>, <em class="sig-param">best_permutation_key='SDR'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/bss_eval.html#BSSEvalScale"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.BSSEvalScale" title="Permalink to this definition">¶</a></dt>
<dd><p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#nussl.evaluation.BSSEvalScale.evaluate_helper" title="nussl.evaluation.BSSEvalScale.evaluate_helper"><code class="xref py py-obj docutils literal notranslate"><span class="pre">evaluate_helper</span></code></a>(references, estimates[, …])</p></td>
<td><p>Implements evaluation using new BSSEval metrics [1].</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#nussl.evaluation.BSSEvalScale.preprocess" title="nussl.evaluation.BSSEvalScale.preprocess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code></a>()</p></td>
<td><p>Scale invariant metrics expects zero-mean centered references and sources.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="nussl.evaluation.BSSEvalScale.evaluate_helper">
<code class="sig-name descname">evaluate_helper</code><span class="sig-paren">(</span><em class="sig-param">references</em>, <em class="sig-param">estimates</em>, <em class="sig-param">compute_sir_sar=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/bss_eval.html#BSSEvalScale.evaluate_helper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.BSSEvalScale.evaluate_helper" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements evaluation using new BSSEval metrics [1]. This computes every
metric described in [1], including:</p>
<ul class="simple">
<li><p>SI-SDR: Scale-invariant source-to-distortion ratio. Higher is better.</p></li>
<li><p>SI-SIR: Scale-invariant source-to-interference ratio. Higher is better.</p></li>
<li><p>SI-SAR: Scale-invariant source-to-artifact ratio. Higher is better.</p></li>
<li><p>SD-SDR: Scale-dependent source-to-distortion ratio. Higher is better.</p></li>
<li><p>SNR: Signal-to-noise ratio. Higher is better.</p></li>
<li><p>SRR: The source-to-rescaled-source ratio. This corresponds to
a term that punishes the estimate if its scale is off relative
to the reference. This is an unnumbered equation in [1], but
is the term on page 2, second column, second to last line:
||s - alpha*s||**2. s is factored out. Higher is better.</p></li>
<li><p>SI-SDRi: Improvement in SI-SDR over using the mixture as the estimate. Higher
is better.</p></li>
<li><p>SD-SDRi: Improvement in SD-SDR over using the mixture as the estimate. Higher
is better.</p></li>
<li><p>SNRi: Improvement in SNR over using the mixture as the estimate. Higher is
better.</p></li>
</ul>
<p>Note:</p>
<p>If <cite>compute_sir_sar = False</cite>, then you’ll get <cite>np.nan</cite> for SI-SIR and
SI-SAR!</p>
<p>References:</p>
<p>[1] Le Roux, J., Wisdom, S., Erdogan, H., & Hershey, J. R.
(2019, May). SDR–half-baked or well done?. In ICASSP 2019-2019 IEEE
International Conference on Acoustics, Speech and Signal
Processing (ICASSP) (pp. 626-630). IEEE.</p>
</dd></dl>
<dl class="method">
<dt id="nussl.evaluation.BSSEvalScale.preprocess">
<code class="sig-name descname">preprocess</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/bss_eval.html#BSSEvalScale.preprocess"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.BSSEvalScale.preprocess" title="Permalink to this definition">¶</a></dt>
<dd><p>Scale invariant metrics expects zero-mean centered references and sources.</p>
</dd></dl>
</dd></dl>
<dl class="function">
<dt id="nussl.evaluation.scale_bss_eval">
<code class="sig-prename descclassname">nussl.evaluation.</code><code class="sig-name descname">scale_bss_eval</code><span class="sig-paren">(</span><em class="sig-param">references</em>, <em class="sig-param">estimate</em>, <em class="sig-param">mixture</em>, <em class="sig-param">idx</em>, <em class="sig-param">compute_sir_sar=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/bss_eval.html#scale_bss_eval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.scale_bss_eval" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes metrics for references[idx] relative to the
chosen estimates. This only works for mono audio. Each
channel should be done independently when calling this
function. Lovingly borrowed from Gordon Wichern and
Jonathan Le Roux at Mitsubishi Electric Research Labs.</p>
<p>This returns 9 numbers (in this order):</p>
<ul class="simple">
<li><p>SI-SDR: Scale-invariant source-to-distortion ratio. Higher is better.</p></li>
<li><p>SI-SIR: Scale-invariant source-to-interference ratio. Higher is better.</p></li>
<li><p>SI-SAR: Scale-invariant source-to-artifact ratio. Higher is better.</p></li>
<li><p>SD-SDR: Scale-dependent source-to-distortion ratio. Higher is better.</p></li>
<li><p>SNR: Signal-to-noise ratio. Higher is better.</p></li>
<li><p>SRR: The source-to-rescaled-source ratio. This corresponds to
a term that punishes the estimate if its scale is off relative
to the reference. This is an unnumbered equation in [1], but
is the term on page 2, second column, second to last line:
||s - alpha*s||**2. s here is factored out. Higher is better.</p></li>
<li><p>SI-SDRi: Improvement in SI-SDR over using the mixture as the estimate.</p></li>
<li><p>SD-SDRi: Improvement in SD-SDR over using the mixture as the estimate.</p></li>
<li><p>SNRi: Improvement in SNR over using the mixture as the estimate.</p></li>
</ul>
<p>References:</p>
<dl class="simple">
<dt>[1] Le Roux, J., Wisdom, S., Erdogan, H., & Hershey, J. R.</dt><dd><p>(2019, May). SDR–half-baked or well done?. In ICASSP 2019-2019 IEEE
International Conference on Acoustics, Speech and Signal
Processing (ICASSP) (pp. 626-630). IEEE.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>references</strong> (<em>np.ndarray</em>) – object containing the
references data. Of shape (n_samples, n_sources).</p></li>
<li><p><strong>estimate</strong> (<em>np.ndarray</em>) – object containing the
estimate data. Of shape (n_samples, 1).</p></li>
<li><p><strong>mixture</strong> (<em>np.ndarray</em>) – objct containingthe
mixture data. Of shape (n_samples, 1).</p></li>
<li><p><strong>idx</strong> (<em>int</em>) – Which reference to compute metrics against.</p></li>
<li><p><strong>compute_sir_sar</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether or not to compute SIR/SAR
metrics, which can be computationally expensive and may not be
relevant for your evaluation. Defaults to True</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>SI-SDR, SI-SIR, SI-SAR, SD-SDR, SNR, SRR, SI-SDRi, SD-SDRi, SNRi</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>tuple</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="bssevalv4-museval">
<h2>BSSEvalV4 (museval)<a class="headerlink" href="#bssevalv4-museval" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="nussl.evaluation.BSSEvalV4">
<em class="property">class </em><code class="sig-prename descclassname">nussl.evaluation.</code><code class="sig-name descname">BSSEvalV4</code><span class="sig-paren">(</span><em class="sig-param">true_sources_list</em>, <em class="sig-param">estimated_sources_list</em>, <em class="sig-param">source_labels=None</em>, <em class="sig-param">compute_permutation=False</em>, <em class="sig-param">best_permutation_key='SDR'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/bss_eval.html#BSSEvalV4"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.BSSEvalV4" title="Permalink to this definition">¶</a></dt>
<dd><p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#nussl.evaluation.BSSEvalV4.evaluate_helper" title="nussl.evaluation.BSSEvalV4.evaluate_helper"><code class="xref py py-obj docutils literal notranslate"><span class="pre">evaluate_helper</span></code></a>(references, estimates, **kwargs)</p></td>
<td><p>Implements evaluation using museval.metrics.bss_eval</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="nussl.evaluation.BSSEvalV4.evaluate_helper">
<code class="sig-name descname">evaluate_helper</code><span class="sig-paren">(</span><em class="sig-param">references</em>, <em class="sig-param">estimates</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/bss_eval.html#BSSEvalV4.evaluate_helper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.BSSEvalV4.evaluate_helper" title="Permalink to this definition">¶</a></dt>
<dd><p>Implements evaluation using museval.metrics.bss_eval</p>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="precision-and-recall-on-masks">
<h2>Precision and recall on masks<a class="headerlink" href="#precision-and-recall-on-masks" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="nussl.evaluation.PrecisionRecallFScore">
<em class="property">class </em><code class="sig-prename descclassname">nussl.evaluation.</code><code class="sig-name descname">PrecisionRecallFScore</code><span class="sig-paren">(</span><em class="sig-param">true_sources_list</em>, <em class="sig-param">estimated_sources_list</em>, <em class="sig-param">source_labels=None</em>, <em class="sig-param">compute_permutation=False</em>, <em class="sig-param">best_permutation_key='F1-Score'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/precision_recall_fscore.html#PrecisionRecallFScore"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.PrecisionRecallFScore" title="Permalink to this definition">¶</a></dt>
<dd><p>This class provides common statistical metrics for determining how well a source separation algorithm in nussl was
able to create a binary mask compared to a known binary mask. The metrics used here are
<a class="reference external" href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision, Recall</a>,
<a class="reference external" href="https://en.wikipedia.org/wiki/F1_score">F-Score</a> (sometimes called F-measure or F1-score), and Accuracy
(though this is not reflected in the name of the class, it is simply <code class="docutils literal notranslate"><span class="pre">#</span> <span class="pre">correct</span> <span class="pre">/</span> <span class="pre">total</span></code>).</p>
<p class="rubric">Notes</p>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#nussl.evaluation.PrecisionRecallFScore.evaluate_helper" title="nussl.evaluation.PrecisionRecallFScore.evaluate_helper"><code class="xref py py-obj docutils literal notranslate"><span class="pre">evaluate_helper</span></code></a>(references, estimates, **kwargs)</p></td>
<td><p>Determines the precision, recall, f-score, and accuracy of each <span class="xref std std-ref">binary_mask</span> object in <code class="docutils literal notranslate"><span class="pre">true_sources_mask_list</span></code> and <code class="docutils literal notranslate"><span class="pre">estimated_sources_mask_list</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#nussl.evaluation.PrecisionRecallFScore.preprocess" title="nussl.evaluation.PrecisionRecallFScore.preprocess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code></a>()</p></td>
<td><p>Takes the objects contained in <cite>true_sources_list</cite> and <cite>estimated_sources_list</cite> and processes them into numpy arrays that have shape (…, n_channels, n_sources).</p></td>
</tr>
</tbody>
</table>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">PrecisionRecallFScore</span></code> can only be run using <span class="xref std std-ref">binary_mask</span> objects. The constructor expects a list of</p></li>
</ul>
<p><span class="xref std std-ref">binary_mask</span> objects for both the ground truth sources and the estimated sources.
* <code class="docutils literal notranslate"><span class="pre">PrecisionRecallFScore</span></code> does not calculate the correct permutation of the estimated and ground truth sources;
they are expected to be in the correct order when they are passed into <code class="docutils literal notranslate"><span class="pre">PrecisionRecallFScore</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>true_sources_mask_list</strong> (<em>list</em>) – List of <span class="xref std std-ref">binary_mask</span> objects representing the ground truth sources.</p></li>
<li><p><strong>estimated_sources_mask_list</strong> (<em>list</em>) – List of <span class="xref std std-ref">binary_mask</span> objects representing the estimates from a source
separation object</p></li>
<li><p><strong>source_labels</strong> (<em>list</em><em>) </em><em>(</em><em>Optional</em>) – List of <code class="docutils literal notranslate"><span class="pre">str</span></code> with labels for each source. If no labels are provided, sources
will be labeled <code class="docutils literal notranslate"><span class="pre">Source</span> <span class="pre">0,</span> <span class="pre">Source</span> <span class="pre">1,</span> <span class="pre">...</span></code> etc.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="nussl.evaluation.PrecisionRecallFScore.evaluate_helper">
<code class="sig-name descname">evaluate_helper</code><span class="sig-paren">(</span><em class="sig-param">references</em>, <em class="sig-param">estimates</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/precision_recall_fscore.html#PrecisionRecallFScore.evaluate_helper"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.PrecisionRecallFScore.evaluate_helper" title="Permalink to this definition">¶</a></dt>
<dd><p>Determines the precision, recall, f-score, and accuracy of each <span class="xref std std-ref">binary_mask</span> object in
<code class="docutils literal notranslate"><span class="pre">true_sources_mask_list</span></code> and <code class="docutils literal notranslate"><span class="pre">estimated_sources_mask_list</span></code>. Returns a list of results that is
formatted like so:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="p">[</span>
<span class="p">{</span><span class="s1">'Accuracy'</span><span class="p">:</span> <span class="mf">0.83</span><span class="p">,</span>
<span class="s1">'Precision'</span><span class="p">:</span> <span class="mf">0.78</span><span class="p">,</span>
<span class="s1">'Recall'</span><span class="p">:</span> <span class="mf">0.81</span><span class="p">,</span>
<span class="s1">'F1-Score'</span><span class="p">:</span> <span class="mf">0.77</span> <span class="p">},</span>
<span class="p">{</span><span class="s1">'Accuracy'</span><span class="p">:</span> <span class="mf">0.22</span><span class="p">,</span>
<span class="s1">'Precision'</span><span class="p">:</span> <span class="mf">0.12</span><span class="p">,</span>
<span class="s1">'Recall'</span><span class="p">:</span> <span class="mf">0.15</span><span class="p">,</span>
<span class="s1">'F1-Score'</span><span class="p">:</span> <span class="mf">0.19</span> <span class="p">}</span>
<span class="p">]</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A list of scores that contains accuracy, precision, recall, and F1-score
of between the list of <span class="xref std std-ref">binary_mask</span> objects in both <code class="docutils literal notranslate"><span class="pre">true_sources_mask_list</span></code>
and <code class="docutils literal notranslate"><span class="pre">estimated_sources_mask_list</span></code>.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>self.scores (dict)</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="nussl.evaluation.PrecisionRecallFScore.preprocess">
<code class="sig-name descname">preprocess</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/precision_recall_fscore.html#PrecisionRecallFScore.preprocess"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.PrecisionRecallFScore.preprocess" title="Permalink to this definition">¶</a></dt>
<dd><p>Takes the objects contained in <cite>true_sources_list</cite> and <cite>estimated_sources_list</cite>
and processes them into numpy arrays that have shape
(…, n_channels, n_sources).</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>references, estimates in that order as np.ndarrays.</p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Make sure to return the preprocessed data in the order
(references, estimates)!</p>
</div>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="aggregators">
<h2>Aggregators<a class="headerlink" href="#aggregators" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="nussl.evaluation.aggregate_score_files">
<code class="sig-prename descclassname">nussl.evaluation.</code><code class="sig-name descname">aggregate_score_files</code><span class="sig-paren">(</span><em class="sig-param">json_files</em>, <em class="sig-param">aggregator=<function nanmedian></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/nussl/evaluation/report_card.html#aggregate_score_files"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nussl.evaluation.aggregate_score_files" title="Permalink to this definition">¶</a></dt>
<dd><p>Takes a list of json files output by an Evaluation method in nussl
and aggregates all the metrics into a Pandas dataframe. Sample
output:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span> SDR SIR SAR
drums oracle0.json 9.086025 15.025801 10.362709
random0.json -6.539877 -6.087538 3.508338
oracle1.json 9.591432 14.335700 11.365882
random1.json -1.358840 -0.993666 9.577297
bass oracle0.json 7.936720 12.843092 9.631929
random0.json -4.190299 -3.730649 5.802003
oracle1.json 8.581090 12.513445 10.831370
random1.json 0.365171 0.697621 11.693103
other oracle0.json 2.024207 6.133359 4.158805
random0.json -9.857085 -9.481909 0.965199
oracle1.json 3.961383 6.861785 7.085745
random1.json -4.042277 -3.707997 7.260934
vocals oracle0.json 12.169686 16.650161 14.085037
random0.json -2.440166 -1.884026 6.760966
oracle1.json 12.409913 16.248470 14.725983
random1.json 1.609577 1.958037 12.738970
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>json_files</strong> (<em>list</em>) – List of JSON files that will be parsed for metrics.</p></li>
<li><p><strong>aggregator</strong> (<em>[</em><em>type</em><em>]</em><em>, </em><em>optional</em>) – How to aggregate results within a single
track. Defaults to np.median.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Pandas dataframe containing the aggregated metrics.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>pd.DataFrame</p>
</dd>
</dl>
</dd></dl>
</div>
</div>
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