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<a href="https://github.com/apache/mxnet/tree/master/edit/master/docs/api/metric.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
<!–- Licensed to the Apache Software Foundation (ASF) under one –> <!–- or more contributor license agreements. See the NOTICE file –> <!–- distributed with this work for additional information –> <!–- regarding copyright ownership. The ASF licenses this file –> <!–- to you under the Apache License, Version 2.0 (the –> <!–- "License"); you may not use this file except in compliance –> <!–- with the License. You may obtain a copy of the License at –>
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<!–- Unless required by applicable law or agreed to in writing, –> <!–- software distributed under the License is distributed on an –> <!–- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY –> <!–- KIND, either express or implied. See the License for the –> <!–- specific language governing permissions and limitations –> <!–- under the License. –>
<p><a id='Evaluation-Metrics-1'></a></p>
<h1 id="evaluation-metrics">Evaluation Metrics</h1>
<p>Evaluation metrics provide a way to evaluate the performance of a learned model. This is typically used during training to monitor performance on the validation set.</p>
<p><a id='MXNet.mx.ACE' href='#MXNet.mx.ACE'>#</a>
<strong><code>MXNet.mx.ACE</code></strong> &mdash; <em>Type</em>.</p>
<pre><code class="julia">ACE
</code></pre>
<p>Calculates the averaged cross-entropy (logloss) for classification.</p>
<p><strong>Arguments:</strong></p>
<ul>
<li><code>eps::Float64</code>: Prevents returning <code>Inf</code> if <code>p = 0</code>.</li>
</ul>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L348-L355' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.AbstractEvalMetric' href='#MXNet.mx.AbstractEvalMetric'>#</a>
<strong><code>MXNet.mx.AbstractEvalMetric</code></strong> &mdash; <em>Type</em>.</p>
<pre><code class="julia">AbstractEvalMetric
</code></pre>
<p>The base class for all evaluation metrics. The sub-types should implement the following interfaces:</p>
<ul>
<li><a href="./#MXNet.mx.update!-Union{Tuple{T}, Tuple{T,AbstractArray{#s97,1} where #s97&lt;:NDArray,AbstractArray{#s97,1} where #s97&lt;:NDArray}} where T&lt;:AbstractEvalMetric"><code>update!</code></a></li>
<li><a href="./#MXNet.mx.reset!-Tuple{AbstractEvalMetric}"><code>reset!</code></a></li>
<li><a href="../io/#Base.get"><code>get</code></a></li>
</ul>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L18-L27' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.Accuracy' href='#MXNet.mx.Accuracy'>#</a>
<strong><code>MXNet.mx.Accuracy</code></strong> &mdash; <em>Type</em>.</p>
<pre><code class="julia">Accuracy
</code></pre>
<p>Multiclass classification accuracy.</p>
<p>Calculates the mean accuracy per sample for softmax in one dimension. For a multi-dimensional softmax the mean accuracy over all dimensions is calculated.</p>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L177-L184' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.MSE' href='#MXNet.mx.MSE'>#</a>
<strong><code>MXNet.mx.MSE</code></strong> &mdash; <em>Type</em>.</p>
<pre><code class="julia">MSE
</code></pre>
<p>Mean Squared Error.</p>
<p>Calculates the mean squared error regression loss. Requires that label and prediction have the same shape.</p>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L233-L240' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.MultiACE' href='#MXNet.mx.MultiACE'>#</a>
<strong><code>MXNet.mx.MultiACE</code></strong> &mdash; <em>Type</em>.</p>
<pre><code class="julia">MultiACE
</code></pre>
<p>Calculates the averaged cross-entropy per class and overall (see <a href="./#MXNet.mx.ACE"><code>ACE</code></a>). This can be used to quantify the influence of different classes on the overall loss.</p>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L410-L415' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.MultiMetric' href='#MXNet.mx.MultiMetric'>#</a>
<strong><code>MXNet.mx.MultiMetric</code></strong> &mdash; <em>Type</em>.</p>
<pre><code class="julia">MultiMetric(metrics::Vector{AbstractEvalMetric})
</code></pre>
<p>Combine multiple metrics in one and get a result for all of them.</p>
<p><strong>Usage</strong></p>
<p>To calculate both mean-squared error <a href="./#MXNet.mx.Accuracy"><code>Accuracy</code></a> and log-loss <a href="./#MXNet.mx.ACE"><code>ACE</code></a>:</p>
<pre><code class="julia"> mx.fit(..., eval_metric = mx.MultiMetric([mx.Accuracy(), mx.ACE()]))
</code></pre>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L116-L126' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.NMSE' href='#MXNet.mx.NMSE'>#</a>
<strong><code>MXNet.mx.NMSE</code></strong> &mdash; <em>Type</em>.</p>
<pre><code class="julia">NMSE
</code></pre>
<p>Normalized Mean Squared Error</p>
<p>
<script type="math/tex; mode=display">
\sum_i (\frac{label_i - pred_i}{label_i})^2
</script>
</p>
<p>Note that there are various ways to do the <em>normalization</em>. It depends on your own context. Please judge the problem setting you have first. If the current implementation do not suitable for you, feel free to file it on GitHub.</p>
<p>Let me show you a use case of this kind of normalization:</p>
<p>Bob is training a network for option pricing. The option pricing problem is a regression problem (pirce predicting). There are lots of option contracts on same target stock but different strike price. For example, there is a stock <code>S</code>; it's market price is 1000. And, there are two call option contracts with different strike price. Assume Bob obtains the outcome as following table:</p>
<pre><code>+--------+----------------+----------------+--------------+
| | Strike Price | Market Price | Pred Price |
+--------+----------------+----------------+--------------+
| Op 1 | 1500 | 100 | 80 |
+--------+----------------+----------------+--------------+
| Op 2 | 500 | 10 | 8 |
+--------+----------------+----------------+--------------+
</code></pre>
<p>Now, obviously, Bob will calculate the normalized MSE as:</p>
<p>
<script type="math/tex; mode=display">
(\frac{100 - 80}{100})^2
\text{ vs }
(\frac{10 - 8}{10}) ^2
</script>
</p>
<p>Both of the pred prices got the same degree of error.</p>
<p>For more discussion about normalized MSE, please see <a href="https://github.com/dmlc/MXNet.jl/pull/211">#211</a> also.</p>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L272' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.SeqMetric' href='#MXNet.mx.SeqMetric'>#</a>
<strong><code>MXNet.mx.SeqMetric</code></strong> &mdash; <em>Type</em>.</p>
<pre><code class="julia">SeqMetric(metrics::Vector{AbstractEvalMetric})
</code></pre>
<p>Apply a different metric to each output. This is especially useful for <code>mx.Group</code>.</p>
<p><strong>Usage</strong></p>
<p>Calculate accuracy <a href="./#MXNet.mx.Accuracy"><code>Accuracy</code></a> for the first output and log-loss <a href="./#MXNet.mx.ACE"><code>ACE</code></a> for the second output:</p>
<pre><code class="julia"> mx.fit(..., eval_metric = mx.SeqMetric([mx.Accuracy(), mx.ACE()]))
</code></pre>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L145-L156' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.update!-Union{Tuple{T}, Tuple{T,AbstractArray{#s97,1} where #s97<:NDArray,AbstractArray{#s97,1} where #s97<:NDArray}} where T<:AbstractEvalMetric' href='#MXNet.mx.update!-Union{Tuple{T}, Tuple{T,AbstractArray{#s97,1} where #s97<:NDArray,AbstractArray{#s97,1} where #s97<:NDArray}} where T<:AbstractEvalMetric'>#</a>
<strong><code>MXNet.mx.update!</code></strong> &mdash; <em>Method</em>.</p>
<pre><code class="julia">update!(metric, labels, preds)
</code></pre>
<p>Update and accumulate metrics.</p>
<p><strong>Arguments:</strong></p>
<ul>
<li><code>metric::AbstractEvalMetric</code>: the metric object.</li>
<li><code>labels::Vector{NDArray}</code>: the labels from the data provider.</li>
<li><code>preds::Vector{NDArray}</code>: the outputs (predictions) of the network.</li>
</ul>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L40-L49' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.NullMetric' href='#MXNet.mx.NullMetric'>#</a>
<strong><code>MXNet.mx.NullMetric</code></strong> &mdash; <em>Type</em>.</p>
<pre><code class="julia">NullMetric()
</code></pre>
<p>A metric that calculates nothing. Can be used to ignore an output during training.</p>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L102-L106' class='documenter-source'>source</a><br></p>
<p><a id='Base.get-Tuple{AbstractEvalMetric}' href='#Base.get-Tuple{AbstractEvalMetric}'>#</a>
<strong><code>Base.get</code></strong> &mdash; <em>Method</em>.</p>
<pre><code class="julia">get(metric)
</code></pre>
<p>Get the accumulated metrics.</p>
<p>Returns <code>Vector{Tuple{Base.Symbol, Real}}</code>, a list of name-value pairs. For example, <code>[(:accuracy, 0.9)]</code>.</p>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L92-L99' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.hasNDArraySupport-Tuple{AbstractEvalMetric}' href='#MXNet.mx.hasNDArraySupport-Tuple{AbstractEvalMetric}'>#</a>
<strong><code>MXNet.mx.hasNDArraySupport</code></strong> &mdash; <em>Method</em>.</p>
<pre><code class="julia">hasNDArraySupport(metric) -&gt; Val{true/false}
</code></pre>
<p>Trait for <code>_update_single_output</code> should return <code>Val{true}() if metric can handle</code>NDArray<code>directly and</code>Val{false}()<code>if requires</code>Array`. Metric that work with NDArrays can be async, while native Julia arrays require that we copy the output of the network, which is a blocking operation.</p>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L30-L37' class='documenter-source'>source</a><br></p>
<p><a id='MXNet.mx.reset!-Tuple{AbstractEvalMetric}' href='#MXNet.mx.reset!-Tuple{AbstractEvalMetric}'>#</a>
<strong><code>MXNet.mx.reset!</code></strong> &mdash; <em>Method</em>.</p>
<pre><code class="julia">reset!(metric)
</code></pre>
<p>Reset the accumulation counter.</p>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/metric.jl#L83-L87' class='documenter-source'>source</a><br></p>
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