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---
title: metrics
keywords: fastai
sidebar: home_sidebar
summary: "Useful metrics for training"
---
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<h2 id="Training-metrics">Training metrics<a class="anchor-link" href="#Training-metrics">¶</a></h2>
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<p><em>Metrics</em> for training fastai models are simply functions that take <code>input</code> and <code>target</code> tensors, and return some metric of interest for training. You can write your own metrics by defining a function of that type, and passing it to <a href="/basic_train.html#Learner"><code>Learner</code></a> in the <a href="/metrics.html#metrics"><code>metrics</code></a> parameter, or use one of the following pre-defined functions.</p>
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<h2 id="Predefined-metrics:">Predefined metrics:<a class="anchor-link" href="#Predefined-metrics:">¶</a></h2>
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<h4 id="accuracy" class="doc_header"><code>accuracy</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L25" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#accuracy-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>accuracy</code>(<strong><code>input</code></strong>:<code>Tensor</code>, <strong><code>targs</code></strong>:<code>Tensor</code>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="accuracy-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#accuracy-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>accuracy</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_accuracy</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L44" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_accuracy</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L55" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Computes accuracy with <code>targs</code> when <code>input</code> is bs * n_classes.</p>
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<div markdown="span" class="alert alert-danger" role="alert"><i class="fa fa-danger-circle"></i> <b>Warning: </b>This metric is intended for classification of objects belonging to a single class.</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">])</span> <span class="c1"># bs = 5, n = 2</span>
<span class="n">ys</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">accuracy</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ys</span><span class="p">)</span>
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<pre>tensor(0.6000)</pre>
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<h4 id="accuracy_thresh" class="doc_header"><code>accuracy_thresh</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L32" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#accuracy_thresh-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>accuracy_thresh</code>(<strong><code>y_pred</code></strong>:<code>Tensor</code>, <strong><code>y_true</code></strong>:<code>Tensor</code>, <strong><code>thresh</code></strong>:<code>float</code>=<strong><em><code>0.5</code></em></strong>, <strong><code>sigmoid</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="accuracy_thresh-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#accuracy_thresh-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>accuracy_thresh</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_accuracy_thresh</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L99" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Computes accuracy when <code>y_pred</code> and <code>y_true</code> are the same size.</p>
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<p>Predictions are compared to <code>thresh</code> after <code>sigmoid</code> is maybe applied. Then we count the numbers that match the targets.</p>
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<div markdown="span" class="alert alert-info" role="alert"><i class="fa fa-info-circle"></i> <b>Note: </b>This function is intended for one-hot-encoded targets (often in a multiclassification problem).</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">])</span>
<span class="n">ys</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">accuracy_thresh</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">thresh</span><span class="o">=</span><span class="mf">0.65</span><span class="p">,</span> <span class="n">sigmoid</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>tensor(0.4000)</pre>
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<h4 id="top_k_accuracy" class="doc_header"><code>top_k_accuracy</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L37" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#top_k_accuracy-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>top_k_accuracy</code>(<strong><code>input</code></strong>:<code>Tensor</code>, <strong><code>targs</code></strong>:<code>Tensor</code>, <strong><code>k</code></strong>:<code>int</code>=<strong><em><code>5</code></em></strong>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="top_k_accuracy-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#top_k_accuracy-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>top_k_accuracy</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_top_k_accuracy</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L69" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Computes the Top-k accuracy (target is in the top k predictions).</p>
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<h4 id="dice" class="doc_header"><code>dice</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L53" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#dice-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>dice</code>(<strong><code>input</code></strong>:<code>Tensor</code>, <strong><code>targs</code></strong>:<code>Tensor</code>, <strong><code>iou</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>eps</code></strong>:<code>float</code>=<strong><em><code>1e-08</code></em></strong>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="dice-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#dice-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>dice</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_dice</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L127" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_metrics.py::test_dice_iou</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L137" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems.</p>
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$$dice = \frac{2(TP)}{2(TP) + FP + FN}$$<p>where TP, FP and FN are the number of true positives, false positives and false negatives.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">])</span>
<span class="n">ys</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">dice</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ys</span><span class="p">)</span> <span class="c1"># TP = 2, FP = 1, FN = 1</span>
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<pre>tensor(0.6667)</pre>
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<h4 id="error_rate" class="doc_header"><code>error_rate</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L49" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#error_rate-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>error_rate</code>(<strong><code>input</code></strong>:<code>Tensor</code>, <strong><code>targs</code></strong>:<code>Tensor</code>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="error_rate-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#error_rate-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>error_rate</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_error_rate</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L86" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_error_rate</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L59" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>1 - <a href="/metrics.html#accuracy"><code>accuracy</code></a></p>
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<h4 id="mean_squared_error" class="doc_header"><code>mean_squared_error</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L80" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#mean_squared_error-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>mean_squared_error</code>(<strong><code>pred</code></strong>:<code>Tensor</code>, <strong><code>targ</code></strong>:<code>Tensor</code>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="mean_squared_error-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#mean_squared_error-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>mean_squared_error</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_mse</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L163" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Mean squared error between <code>pred</code> and <code>targ</code>.</p>
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<h4 id="mean_absolute_error" class="doc_header"><code>mean_absolute_error</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L75" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#mean_absolute_error-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>mean_absolute_error</code>(<strong><code>pred</code></strong>:<code>Tensor</code>, <strong><code>targ</code></strong>:<code>Tensor</code>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="mean_absolute_error-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#mean_absolute_error-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>mean_absolute_error</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_mae</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L154" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Mean absolute error between <code>pred</code> and <code>targ</code>.</p>
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<h4 id="mean_squared_logarithmic_error" class="doc_header"><code>mean_squared_logarithmic_error</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L90" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#mean_squared_logarithmic_error-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>mean_squared_logarithmic_error</code>(<strong><code>pred</code></strong>:<code>Tensor</code>, <strong><code>targ</code></strong>:<code>Tensor</code>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="mean_squared_logarithmic_error-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#mean_squared_logarithmic_error-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>mean_squared_logarithmic_error</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_msle</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L182" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Mean squared logarithmic error between <code>pred</code> and <code>targ</code>.</p>
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<h4 id="exp_rmspe" class="doc_header"><code>exp_rmspe</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L68" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#exp_rmspe-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>exp_rmspe</code>(<strong><code>pred</code></strong>:<code>Tensor</code>, <strong><code>targ</code></strong>:<code>Tensor</code>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="exp_rmspe-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#exp_rmspe-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>exp_rmspe</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_exp_rmspe</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L90" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_metrics.py::test_exp_rmspe_num_of_ele</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L94" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Exp RMSE between <code>pred</code> and <code>targ</code>.</p>
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<h4 id="root_mean_squared_error" class="doc_header"><code>root_mean_squared_error</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L85" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#root_mean_squared_error-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>root_mean_squared_error</code>(<strong><code>pred</code></strong>:<code>Tensor</code>, <strong><code>targ</code></strong>:<code>Tensor</code>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="root_mean_squared_error-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#root_mean_squared_error-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>root_mean_squared_error</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_rmse</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L172" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Root mean squared error between <code>pred</code> and <code>targ</code>.</p>
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<h4 id="fbeta" class="doc_header"><code>fbeta</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L13" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#fbeta-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>fbeta</code>(<strong><code>y_pred</code></strong>:<code>Tensor</code>, <strong><code>y_true</code></strong>:<code>Tensor</code>, <strong><code>thresh</code></strong>:<code>float</code>=<strong><em><code>0.2</code></em></strong>, <strong><code>beta</code></strong>:<code>float</code>=<strong><em><code>2</code></em></strong>, <strong><code>eps</code></strong>:<code>float</code>=<strong><em><code>1e-09</code></em></strong>, <strong><code>sigmoid</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="fbeta-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#fbeta-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>fbeta</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_fbeta</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L145" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Computes the f_beta between <code>preds</code> and <code>targets</code></p>
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<p><code>beta</code> determines the value of the fbeta applied, <code>eps</code> is there for numeric stability. If <code>sigmoid=True</code>, a sigmoid is applied to the predictions before comparing them to <code>thresh</code> then to the targets. See the <a href="https://en.wikipedia.org/wiki/F1_score">F1 score wikipedia page</a> for details on the fbeta score.</p>
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$${F_\beta} = (1+\beta^2)\frac{precision \cdot recall}{(\beta^2 \cdot precision) + recall}$$
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">])</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="c1"># TP =2, FP = 1, FN = 1</span>
<span class="n">ys</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">fbeta</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">thresh</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">sigmoid</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>tensor(0.6667)</pre>
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<div markdown="span" class="alert alert-info" role="alert"><i class="fa fa-info-circle"></i> <b>Note: </b>This function is intended for one-hot-encoded targets (often in a multiclassification problem).</div>
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<h4 id="explained_variance" class="doc_header"><code>explained_variance</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L95" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#explained_variance-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>explained_variance</code>(<strong><code>pred</code></strong>:<code>Tensor</code>, <strong><code>targ</code></strong>:<code>Tensor</code>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="explained_variance-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#explained_variance-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>explained_variance</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_explained_variance</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L193" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Explained variance between <code>pred</code> and <code>targ</code>.</p>
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$$ Explained \ Variance = 1 - \frac{Var( targ - pred )}{Var( targ )}$$
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.10</span><span class="p">,</span> <span class="o">.</span><span class="mi">20</span><span class="p">,</span> <span class="o">.</span><span class="mi">30</span><span class="p">,</span> <span class="o">.</span><span class="mi">40</span><span class="p">,</span> <span class="o">.</span><span class="mi">50</span><span class="p">])</span>
<span class="n">ys</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.12</span><span class="p">,</span> <span class="o">.</span><span class="mi">17</span><span class="p">,</span> <span class="o">.</span><span class="mi">25</span><span class="p">,</span> <span class="o">.</span><span class="mi">44</span><span class="p">,</span> <span class="o">.</span><span class="mi">56</span><span class="p">])</span> <span class="c1"># predictions are close to the truth</span>
<span class="n">explained_variance</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ys</span><span class="p">)</span>
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<pre>tensor(0.9374)</pre>
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<h4 id="r2_score" class="doc_header"><code>r2_score</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L101" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#r2_score-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>r2_score</code>(<strong><code>pred</code></strong>:<code>Tensor</code>, <strong><code>targ</code></strong>:<code>Tensor</code>) → <code>Rank0Tensor</code></p>
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<div class="collapse" id="r2_score-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#r2_score-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>r2_score</code>:</p><ul><li><code>pytest -sv tests/test_metrics.py::test_r2_score</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_metrics.py#L204" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>R2 score (coefficient of determination) between <code>pred</code> and <code>targ</code>.</p>
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$$ {R^2} = 1 - \frac{\sum( targ - pred )^2}{\sum( targ - \overline{targ})^2}$$<p>where $\overline{targ}$ is the mean of the targ tensor.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">r2_score</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ys</span><span class="p">)</span>
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<pre>tensor(0.9351)</pre>
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<p>The following metrics are classes, don't forget to instantiate them when you pass them to a <a href="/basic_train.html#Learner"><code>Learner</code></a>.</p>
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<h3 id="RMSE" class="doc_header"><code>class</code> <code>RMSE</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L128" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#RMSE-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>RMSE</code>() :: <a href="/metrics.html#RegMetrics"><code>RegMetrics</code></a></p>
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<div class="collapse" id="RMSE-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#RMSE-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>RMSE</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the root mean squared error.</p>
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<h3 id="ExpRMSPE" class="doc_header"><code>class</code> <code>ExpRMSPE</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L133" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ExpRMSPE-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>ExpRMSPE</code>() :: <a href="/metrics.html#RegMetrics"><code>RegMetrics</code></a></p>
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<div class="collapse" id="ExpRMSPE-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ExpRMSPE-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>ExpRMSPE</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the exponential of the root mean square error.</p>
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<h3 id="Precision" class="doc_header"><code>class</code> <code>Precision</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L208" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Precision-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>Precision</code>(<strong><code>average</code></strong>:<code>Optional</code>[<code>str</code>]=<strong><em><code>'binary'</code></em></strong>, <strong><code>pos_label</code></strong>:<code>int</code>=<strong><em><code>1</code></em></strong>, <strong><code>eps</code></strong>:<code>float</code>=<strong><em><code>1e-09</code></em></strong>) :: <a href="/metrics.html#CMScores"><code>CMScores</code></a></p>
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<div class="collapse" id="Precision-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Precision-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>Precision</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the Precision.</p>
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<h3 id="Recall" class="doc_header"><code>class</code> <code>Recall</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L203" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Recall-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>Recall</code>(<strong><code>average</code></strong>:<code>Optional</code>[<code>str</code>]=<strong><em><code>'binary'</code></em></strong>, <strong><code>pos_label</code></strong>:<code>int</code>=<strong><em><code>1</code></em></strong>, <strong><code>eps</code></strong>:<code>float</code>=<strong><em><code>1e-09</code></em></strong>) :: <a href="/metrics.html#CMScores"><code>CMScores</code></a></p>
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<div class="collapse" id="Recall-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Recall-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>Recall</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the Recall.</p>
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<h3 id="FBeta" class="doc_header"><code>class</code> <code>FBeta</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L214" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#FBeta-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>FBeta</code>(<strong><code>average</code></strong>:<code>Optional</code>[<code>str</code>]=<strong><em><code>'binary'</code></em></strong>, <strong><code>pos_label</code></strong>:<code>int</code>=<strong><em><code>1</code></em></strong>, <strong><code>eps</code></strong>:<code>float</code>=<strong><em><code>1e-09</code></em></strong>, <strong><code>beta</code></strong>:<code>float</code>=<strong><em><code>2</code></em></strong>) :: <a href="/metrics.html#CMScores"><code>CMScores</code></a></p>
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<div class="collapse" id="FBeta-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#FBeta-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>FBeta</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the F<code>beta</code> score.</p>
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<h3 id="R2Score" class="doc_header"><code>class</code> <code>R2Score</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L118" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#R2Score-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>R2Score</code>() :: <a href="/metrics.html#RegMetrics"><code>RegMetrics</code></a></p>
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<div class="collapse" id="R2Score-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#R2Score-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>R2Score</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the R2 score (coefficient of determination).</p>
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<h3 id="ExplainedVariance" class="doc_header"><code>class</code> <code>ExplainedVariance</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L123" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ExplainedVariance-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>ExplainedVariance</code>() :: <a href="/metrics.html#RegMetrics"><code>RegMetrics</code></a></p>
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<div class="collapse" id="ExplainedVariance-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ExplainedVariance-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>ExplainedVariance</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the explained variance.</p>
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<h3 id="MatthewsCorreff" class="doc_header"><code>class</code> <code>MatthewsCorreff</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L255" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#MatthewsCorreff-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>MatthewsCorreff</code>() :: <a href="/metrics.html#ConfusionMatrix"><code>ConfusionMatrix</code></a></p>
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<div class="collapse" id="MatthewsCorreff-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#MatthewsCorreff-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>MatthewsCorreff</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the Matthews correlation coefficient.</p>
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<p>Ref.: <a href="https://github.com/scikit-learn/scikit-learn/blob/bac89c2/sklearn/metrics/classification.py">https://github.com/scikit-learn/scikit-learn/blob/bac89c2/sklearn/metrics/classification.py</a></p>
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<h3 id="KappaScore" class="doc_header"><code>class</code> <code>KappaScore</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L235" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#KappaScore-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>KappaScore</code>(<strong><code>weights</code></strong>:<code>Optional</code>[<code>str</code>]=<strong><em><code>None</code></em></strong>) :: <a href="/metrics.html#ConfusionMatrix"><code>ConfusionMatrix</code></a></p>
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<div class="collapse" id="KappaScore-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#KappaScore-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>KappaScore</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the rate of agreement (Cohens Kappa).</p>
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<p>Ref.: <a href="https://github.com/scikit-learn/scikit-learn/blob/bac89c2/sklearn/metrics/classification.py">https://github.com/scikit-learn/scikit-learn/blob/bac89c2/sklearn/metrics/classification.py</a></p>
<p><a href="/metrics.html#KappaScore"><code>KappaScore</code></a> supports linear and quadratic weights on the off-diagonal cells in the <a href="/metrics.html#ConfusionMatrix"><code>ConfusionMatrix</code></a>, in addition to the default unweighted calculation treating all misclassifications as equally weighted. Leaving <a href="/metrics.html#KappaScore"><code>KappaScore</code></a>'s <code>weights</code> attribute as <code>None</code> returns the unweighted Kappa score. Updating <code>weights</code> to "linear" means off-diagonal ConfusionMatrix elements are weighted in linear proportion to their distance from the diagonal; "quadratic" means weights are squared proportional to their distance from the diagonal.
Specify linear or quadratic weights, if using, by first creating an instance of the metric and then updating the <code>weights</code> attribute, similar to as follows:</p>
<pre><code>kappa = KappaScore()
kappa.weights = "quadratic"
learn = cnn_learner(data, model, metrics=[error_rate, kappa])</code></pre>
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<h3 id="ConfusionMatrix" class="doc_header"><code>class</code> <code>ConfusionMatrix</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L144" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ConfusionMatrix-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>ConfusionMatrix</code>() :: <a href="/callback.html#Callback"><code>Callback</code></a></p>
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<div class="collapse" id="ConfusionMatrix-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ConfusionMatrix-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>ConfusionMatrix</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the confusion matrix.</p>
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<h3 id="MultiLabelFbeta" class="doc_header"><code>class</code> <code>MultiLabelFbeta</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L317" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#MultiLabelFbeta-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>MultiLabelFbeta</code>(<strong><code>beta</code></strong>=<strong><em><code>2</code></em></strong>, <strong><code>eps</code></strong>=<strong><em><code>1e-15</code></em></strong>, <strong><code>thresh</code></strong>=<strong><em><code>0.3</code></em></strong>, <strong><code>sigmoid</code></strong>=<strong><em><code>True</code></em></strong>, <strong><code>average</code></strong>=<strong><em><code>'micro'</code></em></strong>) :: <a href="/callback.html#Callback"><code>Callback</code></a></p>
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<div class="collapse" id="MultiLabelFbeta-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#MultiLabelFbeta-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>MultiLabelFbeta</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the fbeta score for multilabel classification</p>
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<p><a href="/metrics.html#MultiLabelFbeta"><code>MultiLabelFbeta</code></a> implements mutlilabel classification fbeta score similar to <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html">scikit-learn's</a> as a <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a>. Average options: ["micro", "macro", "weighted", "none"]. Intended to use with one-hot encoded targets with 1s and 0s.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_doc</span><span class="p">(</span><span class="n">auc_roc_score</span><span class="p">,</span> <span class="n">title_level</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
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<h3 id="auc_roc_score" class="doc_header"><code>auc_roc_score</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L278" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#auc_roc_score-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>auc_roc_score</code>(<strong><code>input</code></strong>:<code>Tensor</code>, <strong><code>targ</code></strong>:<code>Tensor</code>)</p>
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<div class="collapse" id="auc_roc_score-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#auc_roc_score-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>auc_roc_score</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the area under the receiver operator characteristic (ROC) curve using the trapezoid method. Restricted binary classification tasks.</p>
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<p><a href="/metrics.html#auc_roc_score"><code>auc_roc_score</code></a> computes the AUC score for the ROC curve similarly to <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html">scikit-learn</a> using the trapezoid method, effectively summarizing the curve information in a single number. See <a href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve">Wikipedia's page</a> for more information on this.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">jekyll_note</span><span class="p">(</span><span class="s2">"Instead of passing this method to the learner's metrics directly, make use of the AUROC() class."</span><span class="p">)</span>
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<div markdown="span" class="alert alert-info" role="alert"><i class="fa fa-info-circle"></i> <b>Note: </b>Instead of passing this method to the learner's metrics directly, make use of the AUROC() class.</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_doc</span><span class="p">(</span><span class="n">roc_curve</span><span class="p">,</span> <span class="n">title_level</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
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<h3 id="roc_curve" class="doc_header"><code>roc_curve</code><a href="https://github.com/fastai/fastai/blob/master/fastai/metrics.py#L286" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#roc_curve-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>roc_curve</code>(<strong><code>input</code></strong>:<code>Tensor</code>, <strong><code>targ</code></strong>:<code>Tensor</code>)</p>
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<div class="collapse" id="roc_curve-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#roc_curve-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>roc_curve</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Computes the receiver operator characteristic (ROC) curve by determining the true positive ratio (TPR) and false positive ratio (FPR) for various classification thresholds. Restricted binary classification tasks.</p>
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<p><a href="/metrics.html#roc_curve"><code>roc_curve</code></a> generates the ROC curve similarly to <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html">scikit-learn</a>. See <a href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic">Wikipedia's page</a> for more information on the ROC curve.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">jekyll_note</span><span class="p">(</span><span class="s2">"Instead of passing this method to the learner's metrics directly, make use of the AUROC() class."</span><span class="p">)</span>
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<div markdown="span" class="alert alert-info" role="alert"><i class="fa fa-info-circle"></i> <b>Note: </b>Instead of passing this method to the learner's metrics directly, make use of the AUROC() class.</div>
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