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<span class="mdl-layout-title toc">Table Of Contents</span>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/packages/index.html">Packages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
</ul>
</li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
</ul>
</li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
</ul>
</li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li>
</ul>
</li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
</ul>
</li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
</ul>
</li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/optimizer/index.html">Optimizers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/viz/index.html">Visualization</a><ul>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/performance/index.html">Performance</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/performance/compression/index.html">Compression</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
<li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html#Improving-accuracy-with-Intel®-Neural-Compressor">Improving accuracy with Intel® Neural Compressor</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
</ul>
</li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li>
</ul>
</li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/tvm.html">Use TVM</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li>
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<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../../tutorials/packages/index.html">Packages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
</ul>
</li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
</ul>
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<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
</ul>
</li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li>
</ul>
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<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
</ul>
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<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
</ul>
</li>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
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<h1>Source code for mxnet.metric</h1><div class="highlight"><pre>
<span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span>
<span class="c1"># or more contributor license agreements. See the NOTICE file</span>
<span class="c1"># distributed with this work for additional information</span>
<span class="c1"># regarding copyright ownership. The ASF licenses this file</span>
<span class="c1"># to you under the Apache License, Version 2.0 (the</span>
<span class="c1"># &quot;License&quot;); you may not use this file except in compliance</span>
<span class="c1"># with the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing,</span>
<span class="c1"># software distributed under the License is distributed on an</span>
<span class="c1"># &quot;AS IS&quot; BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span>
<span class="c1"># KIND, either express or implied. See the License for the</span>
<span class="c1"># specific language governing permissions and limitations</span>
<span class="c1"># under the License.</span>
<span class="c1"># coding: utf-8</span>
<span class="c1"># pylint: disable=no-member, too-many-lines</span>
<span class="sd">&quot;&quot;&quot;Online evaluation metric module.&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
<span class="kn">import</span> <span class="nn">numpy</span>
<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">numeric_types</span><span class="p">,</span> <span class="n">string_types</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">ndarray</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">registry</span>
<div class="viewcode-block" id="check_label_shapes"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.check_label_shapes">[docs]</a><span class="k">def</span> <span class="nf">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">wrap</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Helper function for checking shape of label and prediction</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> wrap : boolean</span>
<span class="sd"> If True, wrap labels/preds in a list if they are single NDArray</span>
<span class="sd"> shape : boolean</span>
<span class="sd"> If True, check the shape of labels and preds;</span>
<span class="sd"> Otherwise only check their length.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">shape</span><span class="p">:</span>
<span class="n">label_shape</span><span class="p">,</span> <span class="n">pred_shape</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">label_shape</span><span class="p">,</span> <span class="n">pred_shape</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">preds</span><span class="o">.</span><span class="n">shape</span>
<span class="k">if</span> <span class="n">label_shape</span> <span class="o">!=</span> <span class="n">pred_shape</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Shape of labels </span><span class="si">{}</span><span class="s2"> does not match shape of &quot;</span>
<span class="s2">&quot;predictions </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">label_shape</span><span class="p">,</span> <span class="n">pred_shape</span><span class="p">))</span>
<span class="k">if</span> <span class="n">wrap</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">labels</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
<span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">preds</span><span class="p">]</span>
<span class="k">return</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span></div>
<div class="viewcode-block" id="EvalMetric"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric">[docs]</a><span class="k">class</span> <span class="nc">EvalMetric</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Base class for all evaluation metrics.</span>
<span class="sd"> .. note::</span>
<span class="sd"> This is a base class that provides common metric interfaces.</span>
<span class="sd"> One should not use this class directly, but instead create new metric</span>
<span class="sd"> classes that extend it.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="o">=</span> <span class="n">output_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">label_names</span> <span class="o">=</span> <span class="n">label_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_has_global_stats</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;has_global_stats&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="n">kwargs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">&quot;EvalMetric: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_name_value</span><span class="p">()))</span>
<div class="viewcode-block" id="EvalMetric.get_config"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric.get_config">[docs]</a> <span class="k">def</span> <span class="nf">get_config</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Save configurations of metric. Can be recreated</span>
<span class="sd"> from configs with metric.create(``**config``)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">config</span><span class="o">.</span><span class="n">update</span><span class="p">({</span>
<span class="s1">&#39;metric&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span>
<span class="s1">&#39;name&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
<span class="s1">&#39;output_names&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">,</span>
<span class="s1">&#39;label_names&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">})</span>
<span class="k">return</span> <span class="n">config</span></div>
<div class="viewcode-block" id="EvalMetric.update_dict"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric.update_dict">[docs]</a> <span class="k">def</span> <span class="nf">update_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Update the internal evaluation with named label and pred</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : OrderedDict of str -&gt; NDArray</span>
<span class="sd"> name to array mapping for labels.</span>
<span class="sd"> preds : OrderedDict of str -&gt; NDArray</span>
<span class="sd"> name to array mapping of predicted outputs.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">pred</span> <span class="o">=</span> <span class="p">[</span><span class="n">pred</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">pred</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">label</span> <span class="o">=</span> <span class="p">[</span><span class="n">label</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">label</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span></div>
<div class="viewcode-block" id="EvalMetric.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<div class="viewcode-block" id="EvalMetric.reset"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the internal evaluation result to initial state.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">=</span> <span class="mf">0.0</span></div>
<div class="viewcode-block" id="EvalMetric.reset_local"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric.reset_local">[docs]</a> <span class="k">def</span> <span class="nf">reset_local</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the local portion of the internal evaluation results to initial state.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.0</span></div>
<div class="viewcode-block" id="EvalMetric.get"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric.get">[docs]</a> <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Gets the current evaluation result.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> names : list of str</span>
<span class="sd"> Name of the metrics.</span>
<span class="sd"> values : list of float</span>
<span class="sd"> Value of the evaluations.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span><span class="p">)</span></div>
<div class="viewcode-block" id="EvalMetric.get_global"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric.get_global">[docs]</a> <span class="k">def</span> <span class="nf">get_global</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Gets the current global evaluation result.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> names : list of str</span>
<span class="sd"> Name of the metrics.</span>
<span class="sd"> values : list of float</span>
<span class="sd"> Value of the evaluations.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_has_global_stats</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get</span><span class="p">()</span></div>
<div class="viewcode-block" id="EvalMetric.get_name_value"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric.get_name_value">[docs]</a> <span class="k">def</span> <span class="nf">get_name_value</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns zipped name and value pairs.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of tuples</span>
<span class="sd"> A (name, value) tuple list.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="n">value</span> <span class="o">=</span> <span class="p">[</span><span class="n">value</span><span class="p">]</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">))</span></div>
<div class="viewcode-block" id="EvalMetric.get_global_name_value"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.EvalMetric.get_global_name_value">[docs]</a> <span class="k">def</span> <span class="nf">get_global_name_value</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns zipped name and value pairs for global results.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of tuples</span>
<span class="sd"> A (name, value) tuple list.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_has_global_stats</span><span class="p">:</span>
<span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_global</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="n">value</span> <span class="o">=</span> <span class="p">[</span><span class="n">value</span><span class="p">]</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_name_value</span><span class="p">()</span></div></div>
<span class="c1"># pylint: disable=invalid-name</span>
<span class="n">register</span> <span class="o">=</span> <span class="n">registry</span><span class="o">.</span><span class="n">get_register_func</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">,</span> <span class="s1">&#39;metric&#39;</span><span class="p">)</span>
<span class="n">alias</span> <span class="o">=</span> <span class="n">registry</span><span class="o">.</span><span class="n">get_alias_func</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">,</span> <span class="s1">&#39;metric&#39;</span><span class="p">)</span>
<span class="n">_create</span> <span class="o">=</span> <span class="n">registry</span><span class="o">.</span><span class="n">get_create_func</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">,</span> <span class="s1">&#39;metric&#39;</span><span class="p">)</span>
<span class="c1"># pylint: enable=invalid-name</span>
<div class="viewcode-block" id="create"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.create">[docs]</a><span class="k">def</span> <span class="nf">create</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Creates evaluation metric from metric names or instances of EvalMetric</span>
<span class="sd"> or a custom metric function.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> metric : str or callable</span>
<span class="sd"> Specifies the metric to create.</span>
<span class="sd"> This argument must be one of the below:</span>
<span class="sd"> - Name of a metric.</span>
<span class="sd"> - An instance of `EvalMetric`.</span>
<span class="sd"> - A list, each element of which is a metric or a metric name.</span>
<span class="sd"> - An evaluation function that computes custom metric for a given batch of</span>
<span class="sd"> labels and predictions.</span>
<span class="sd"> *args : list</span>
<span class="sd"> Additional arguments to metric constructor.</span>
<span class="sd"> Only used when metric is str.</span>
<span class="sd"> **kwargs : dict</span>
<span class="sd"> Additional arguments to metric constructor.</span>
<span class="sd"> Only used when metric is str</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; def custom_metric(label, pred):</span>
<span class="sd"> ... return np.mean(np.abs(label - pred))</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; metric1 = mx.metric.create(&#39;acc&#39;)</span>
<span class="sd"> &gt;&gt;&gt; metric2 = mx.metric.create(custom_metric)</span>
<span class="sd"> &gt;&gt;&gt; metric3 = mx.metric.create([metric1, metric2, &#39;rmse&#39;])</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">callable</span><span class="p">(</span><span class="n">metric</span><span class="p">):</span>
<span class="k">return</span> <span class="n">CustomMetric</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="n">composite_metric</span> <span class="o">=</span> <span class="n">CompositeEvalMetric</span><span class="p">()</span>
<span class="k">for</span> <span class="n">child_metric</span> <span class="ow">in</span> <span class="n">metric</span><span class="p">:</span>
<span class="n">composite_metric</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">create</span><span class="p">(</span><span class="n">child_metric</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
<span class="k">return</span> <span class="n">composite_metric</span>
<span class="k">return</span> <span class="n">_create</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="CompositeEvalMetric"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric">[docs]</a><span class="nd">@register</span>
<span class="nd">@alias</span><span class="p">(</span><span class="s1">&#39;composite&#39;</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">CompositeEvalMetric</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Manages multiple evaluation metrics.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> metrics : list of EvalMetric</span>
<span class="sd"> List of child metrics.</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array([0, 1, 1])]</span>
<span class="sd"> &gt;&gt;&gt; eval_metrics_1 = mx.metric.Accuracy()</span>
<span class="sd"> &gt;&gt;&gt; eval_metrics_2 = mx.metric.F1()</span>
<span class="sd"> &gt;&gt;&gt; eval_metrics = mx.metric.CompositeEvalMetric()</span>
<span class="sd"> &gt;&gt;&gt; for child_metric in [eval_metrics_1, eval_metrics_2]:</span>
<span class="sd"> &gt;&gt;&gt; eval_metrics.add(child_metric)</span>
<span class="sd"> &gt;&gt;&gt; eval_metrics.update(labels = labels, preds = predicts)</span>
<span class="sd"> &gt;&gt;&gt; print eval_metrics.get()</span>
<span class="sd"> ([&#39;accuracy&#39;, &#39;f1&#39;], [0.6666666666666666, 0.8])</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;composite&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CompositeEvalMetric</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">if</span> <span class="n">metrics</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">metrics</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">create</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">metrics</span><span class="p">]</span>
<div class="viewcode-block" id="CompositeEvalMetric.add"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric.add">[docs]</a> <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Adds a child metric.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> metric</span>
<span class="sd"> A metric instance.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create</span><span class="p">(</span><span class="n">metric</span><span class="p">))</span></div>
<div class="viewcode-block" id="CompositeEvalMetric.get_metric"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric.get_metric">[docs]</a> <span class="k">def</span> <span class="nf">get_metric</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns a child metric.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> index : int</span>
<span class="sd"> Index of child metric in the list of metrics.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
<span class="k">return</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Metric index </span><span class="si">{}</span><span class="s2"> is out of range 0 and </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">index</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">)))</span></div>
<div class="viewcode-block" id="CompositeEvalMetric.update_dict"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric.update_dict">[docs]</a> <span class="k">def</span> <span class="nf">update_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> <span class="c1"># pylint: disable=arguments-differ</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">labels</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">if</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">])</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">preds</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">if</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">])</span>
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span>
<span class="n">metric</span><span class="o">.</span><span class="n">update_dict</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">)</span></div>
<div class="viewcode-block" id="CompositeEvalMetric.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span>
<span class="n">metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">)</span></div>
<div class="viewcode-block" id="CompositeEvalMetric.reset"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the internal evaluation result to initial state.&quot;&quot;&quot;</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span>
<span class="n">metric</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
<span class="k">pass</span></div>
<div class="viewcode-block" id="CompositeEvalMetric.reset_local"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric.reset_local">[docs]</a> <span class="k">def</span> <span class="nf">reset_local</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the local portion of the internal evaluation results to initial state.&quot;&quot;&quot;</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span>
<span class="n">metric</span><span class="o">.</span><span class="n">reset_local</span><span class="p">()</span>
<span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
<span class="k">pass</span></div>
<div class="viewcode-block" id="CompositeEvalMetric.get"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric.get">[docs]</a> <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns the current evaluation result.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> names : list of str</span>
<span class="sd"> Name of the metrics.</span>
<span class="sd"> values : list of float</span>
<span class="sd"> Value of the evaluations.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">names</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">values</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span>
<span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span>
<span class="n">value</span> <span class="o">=</span> <span class="p">[</span><span class="n">value</span><span class="p">]</span>
<span class="n">names</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="n">values</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span></div>
<div class="viewcode-block" id="CompositeEvalMetric.get_global"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric.get_global">[docs]</a> <span class="k">def</span> <span class="nf">get_global</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns the current evaluation result.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> names : list of str</span>
<span class="sd"> Name of the metrics.</span>
<span class="sd"> values : list of float</span>
<span class="sd"> Value of the evaluations.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">names</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">values</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span>
<span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">get_global</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span>
<span class="n">value</span> <span class="o">=</span> <span class="p">[</span><span class="n">value</span><span class="p">]</span>
<span class="n">names</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="n">values</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span></div>
<div class="viewcode-block" id="CompositeEvalMetric.get_config"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CompositeEvalMetric.get_config">[docs]</a> <span class="k">def</span> <span class="nf">get_config</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">config</span> <span class="o">=</span> <span class="nb">super</span><span class="p">(</span><span class="n">CompositeEvalMetric</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">get_config</span><span class="p">()</span>
<span class="n">config</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">&#39;metrics&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">get_config</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="p">]})</span>
<span class="k">return</span> <span class="n">config</span></div></div>
<span class="c1">########################</span>
<span class="c1"># CLASSIFICATION METRICS</span>
<span class="c1">########################</span>
<div class="viewcode-block" id="Accuracy"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Accuracy">[docs]</a><span class="nd">@register</span>
<span class="nd">@alias</span><span class="p">(</span><span class="s1">&#39;acc&#39;</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Accuracy</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes accuracy classification score.</span>
<span class="sd"> The accuracy score is defined as</span>
<span class="sd"> .. math::</span>
<span class="sd"> \\text{accuracy}(y, \\hat{y}) = \\frac{1}{n} \\sum_{i=0}^{n-1}</span>
<span class="sd"> \\text{1}(\\hat{y_i} == y_i)</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : int, default=1</span>
<span class="sd"> The axis that represents classes</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array([0, 1, 1])]</span>
<span class="sd"> &gt;&gt;&gt; acc = mx.metric.Accuracy()</span>
<span class="sd"> &gt;&gt;&gt; acc.update(preds = predicts, labels = labels)</span>
<span class="sd"> &gt;&gt;&gt; print acc.get()</span>
<span class="sd"> (&#39;accuracy&#39;, 0.6666666666666666)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Accuracy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="o">=</span> <span class="n">axis</span>
<div class="viewcode-block" id="Accuracy.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Accuracy.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data with class indices as values, one per sample.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Prediction values for samples. Each prediction value can either be the class index,</span>
<span class="sd"> or a vector of likelihoods for all classes.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="k">if</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
<span class="n">pred_label</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred_label</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span>
<span class="n">pred_label</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
<span class="c1"># flatten before checking shapes to avoid shape miss match</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">flat</span>
<span class="n">pred_label</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">flat</span>
<span class="n">check_label_shapes</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span><span class="p">)</span>
<span class="n">num_correct</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_label</span> <span class="o">==</span> <span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">num_correct</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">num_correct</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_label</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_label</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="TopKAccuracy"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.TopKAccuracy">[docs]</a><span class="nd">@register</span>
<span class="nd">@alias</span><span class="p">(</span><span class="s1">&#39;top_k_accuracy&#39;</span><span class="p">,</span> <span class="s1">&#39;top_k_acc&#39;</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">TopKAccuracy</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes top k predictions accuracy.</span>
<span class="sd"> `TopKAccuracy` differs from Accuracy in that it considers the prediction</span>
<span class="sd"> to be ``True`` as long as the ground truth label is in the top K</span>
<span class="sd"> predicated labels.</span>
<span class="sd"> If `top_k` = ``1``, then `TopKAccuracy` is identical to `Accuracy`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> top_k : int</span>
<span class="sd"> Whether targets are in top k predictions.</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; np.random.seed(999)</span>
<span class="sd"> &gt;&gt;&gt; top_k = 3</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array([2, 6, 9, 2, 3, 4, 7, 8, 9, 6])]</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array(np.random.rand(10, 10))]</span>
<span class="sd"> &gt;&gt;&gt; acc = mx.metric.TopKAccuracy(top_k=top_k)</span>
<span class="sd"> &gt;&gt;&gt; acc.update(labels, predicts)</span>
<span class="sd"> &gt;&gt;&gt; print acc.get()</span>
<span class="sd"> (&#39;top_k_accuracy&#39;, 0.3)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">top_k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;top_k_accuracy&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">TopKAccuracy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">top_k</span><span class="o">=</span><span class="n">top_k</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">top_k</span> <span class="o">=</span> <span class="n">top_k</span>
<span class="k">assert</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">top_k</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">),</span> <span class="s1">&#39;Please use Accuracy if top_k is no more than 1&#39;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+=</span> <span class="s1">&#39;_</span><span class="si">%d</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">top_k</span>
<div class="viewcode-block" id="TopKAccuracy.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.TopKAccuracy.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;Predictions should be no more than 2 dims&#39;</span>
<span class="c1"># Using argpartition here instead of argsort is safe because</span>
<span class="c1"># we do not care about the order of top k elements. It is</span>
<span class="c1"># much faster, which is important since that computation is</span>
<span class="c1"># single-threaded due to Python GIL.</span>
<span class="n">pred_label</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">argpartition</span><span class="p">(</span><span class="n">pred_label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">),</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">top_k</span><span class="p">)</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
<span class="n">check_label_shapes</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred_label</span><span class="p">)</span>
<span class="n">num_samples</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">num_dims</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">if</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="p">(</span><span class="n">pred_label</span><span class="o">.</span><span class="n">flat</span> <span class="o">==</span> <span class="n">label</span><span class="o">.</span><span class="n">flat</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">elif</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">num_classes</span> <span class="o">=</span> <span class="n">pred_label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">top_k</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">num_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">top_k</span><span class="p">)</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">top_k</span><span class="p">):</span>
<span class="n">num_correct</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_label</span><span class="p">[:,</span> <span class="n">num_classes</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">flat</span> <span class="o">==</span> <span class="n">label</span><span class="o">.</span><span class="n">flat</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">num_correct</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">num_correct</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num_samples</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="n">num_samples</span></div></div>
<span class="k">class</span> <span class="nc">_BinaryClassificationMetrics</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Private container class for classification metric statistics.</span>
<span class="sd"> True/false positive and true/false negative counts are sufficient statistics for various classification metrics.</span>
<span class="sd"> This class provides the machinery to track those statistics across mini-batches of</span>
<span class="sd"> (label, prediction) pairs.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_false_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_false_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_true_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">update_binary_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Update various binary classification counts for a single (label, pred) pair.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> label : `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> pred : `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
<span class="n">pred_label</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">check_label_shapes</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">label</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> currently only supports binary classification.&quot;</span>
<span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">pred_true</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_label</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">pred_false</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">pred_true</span>
<span class="n">label_true</span> <span class="o">=</span> <span class="p">(</span><span class="n">label</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">label_false</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">label_true</span>
<span class="n">true_pos</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_true</span> <span class="o">*</span> <span class="n">label_true</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">false_pos</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_true</span> <span class="o">*</span> <span class="n">label_false</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">false_neg</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_false</span> <span class="o">*</span> <span class="n">label_true</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">true_neg</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_false</span> <span class="o">*</span> <span class="n">label_false</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">+=</span> <span class="n">true_pos</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span> <span class="o">+=</span> <span class="n">true_pos</span>
<span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span> <span class="o">+=</span> <span class="n">false_pos</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_false_positives</span> <span class="o">+=</span> <span class="n">false_pos</span>
<span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span> <span class="o">+=</span> <span class="n">false_neg</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_false_negatives</span> <span class="o">+=</span> <span class="n">false_neg</span>
<span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span> <span class="o">+=</span> <span class="n">true_neg</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_true_negatives</span> <span class="o">+=</span> <span class="n">true_neg</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">precision</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mf">0.</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">global_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_false_positives</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_false_positives</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mf">0.</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">recall</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mf">0.</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">global_recall</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_false_negatives</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_false_negatives</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mf">0.</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">fscore</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">precision</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">recall</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">precision</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">recall</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">precision</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">recall</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mf">0.</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">global_fscore</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_precision</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_recall</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_precision</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_recall</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_precision</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_recall</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mf">0.</span>
<span class="k">def</span> <span class="nf">matthewscc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">use_global</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Calculate the Matthew&#39;s Correlation Coefficent&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">use_global</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_total_examples</span><span class="p">:</span>
<span class="k">return</span> <span class="mf">0.</span>
<span class="n">true_pos</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span><span class="p">)</span>
<span class="n">false_pos</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_false_positives</span><span class="p">)</span>
<span class="n">false_neg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_false_negatives</span><span class="p">)</span>
<span class="n">true_neg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_true_negatives</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_examples</span><span class="p">:</span>
<span class="k">return</span> <span class="mf">0.</span>
<span class="n">true_pos</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span><span class="p">)</span>
<span class="n">false_pos</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span><span class="p">)</span>
<span class="n">false_neg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span><span class="p">)</span>
<span class="n">true_neg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span><span class="p">)</span>
<span class="n">terms</span> <span class="o">=</span> <span class="p">[(</span><span class="n">true_pos</span> <span class="o">+</span> <span class="n">false_pos</span><span class="p">),</span>
<span class="p">(</span><span class="n">true_pos</span> <span class="o">+</span> <span class="n">false_neg</span><span class="p">),</span>
<span class="p">(</span><span class="n">true_neg</span> <span class="o">+</span> <span class="n">false_pos</span><span class="p">),</span>
<span class="p">(</span><span class="n">true_neg</span> <span class="o">+</span> <span class="n">false_neg</span><span class="p">)]</span>
<span class="n">denom</span> <span class="o">=</span> <span class="mf">1.</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">t</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">,</span> <span class="n">terms</span><span class="p">):</span>
<span class="n">denom</span> <span class="o">*=</span> <span class="n">t</span>
<span class="k">return</span> <span class="p">((</span><span class="n">true_pos</span> <span class="o">*</span> <span class="n">true_neg</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">false_pos</span> <span class="o">*</span> <span class="n">false_neg</span><span class="p">))</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">denom</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">total_examples</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span> <span class="o">+</span> \
<span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">global_total_examples</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_false_negatives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_false_positives</span> <span class="o">+</span> \
<span class="bp">self</span><span class="o">.</span><span class="n">global_true_negatives</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span>
<span class="k">def</span> <span class="nf">local_reset_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">reset_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">false_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">false_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">true_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">true_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_false_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_false_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_true_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_true_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<div class="viewcode-block" id="F1"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.F1">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">F1</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes the F1 score of a binary classification problem.</span>
<span class="sd"> The F1 score is equivalent to harmonic mean of the precision and recall,</span>
<span class="sd"> where the best value is 1.0 and the worst value is 0.0. The formula for F1 score is::</span>
<span class="sd"> F1 = 2 * (precision * recall) / (precision + recall)</span>
<span class="sd"> The formula for precision and recall is::</span>
<span class="sd"> precision = true_positives / (true_positives + false_positives)</span>
<span class="sd"> recall = true_positives / (true_positives + false_negatives)</span>
<span class="sd"> .. note::</span>
<span class="sd"> This F1 score only supports binary classification.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> average : str, default &#39;macro&#39;</span>
<span class="sd"> Strategy to be used for aggregating across mini-batches.</span>
<span class="sd"> &quot;macro&quot;: average the F1 scores for each batch.</span>
<span class="sd"> &quot;micro&quot;: compute a single F1 score across all batches.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array([[0.3, 0.7], [0., 1.], [0.4, 0.6]])]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array([0., 1., 1.])]</span>
<span class="sd"> &gt;&gt;&gt; f1 = mx.metric.F1()</span>
<span class="sd"> &gt;&gt;&gt; f1.update(preds = predicts, labels = labels)</span>
<span class="sd"> &gt;&gt;&gt; print f1.get()</span>
<span class="sd"> (&#39;f1&#39;, 0.8)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;f1&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">&quot;macro&quot;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">=</span> <span class="n">average</span>
<span class="bp">self</span><span class="o">.</span><span class="n">metrics</span> <span class="o">=</span> <span class="n">_BinaryClassificationMetrics</span><span class="p">()</span>
<span class="n">EvalMetric</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<div class="viewcode-block" id="F1.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.F1.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">update_binary_stats</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">==</span> <span class="s2">&quot;macro&quot;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">fscore</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">global_fscore</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">reset_stats</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">fscore</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">total_examples</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">global_fscore</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">global_total_examples</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">total_examples</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">global_total_examples</span></div>
<div class="viewcode-block" id="F1.reset"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.F1.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the internal evaluation result to initial state.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">reset_stats</span><span class="p">()</span></div>
<div class="viewcode-block" id="F1.reset_local"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.F1.reset_local">[docs]</a> <span class="k">def</span> <span class="nf">reset_local</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the internal evaluation result to initial state.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">local_reset_stats</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="MCC"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.MCC">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">MCC</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes the Matthews Correlation Coefficient of a binary classification problem.</span>
<span class="sd"> While slower to compute than F1 the MCC can give insight that F1 or Accuracy cannot.</span>
<span class="sd"> For instance, if the network always predicts the same result</span>
<span class="sd"> then the MCC will immeadiately show this. The MCC is also symetric with respect</span>
<span class="sd"> to positive and negative categorization, however, there needs to be both</span>
<span class="sd"> positive and negative examples in the labels or it will always return 0.</span>
<span class="sd"> MCC of 0 is uncorrelated, 1 is completely correlated, and -1 is negatively correlated.</span>
<span class="sd"> .. math::</span>
<span class="sd"> \\text{MCC} = \\frac{ TP \\times TN - FP \\times FN }</span>
<span class="sd"> {\\sqrt{ (TP + FP) ( TP + FN ) ( TN + FP ) ( TN + FN ) } }</span>
<span class="sd"> where 0 terms in the denominator are replaced by 1.</span>
<span class="sd"> .. note::</span>
<span class="sd"> This version of MCC only supports binary classification. See PCC.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> average : str, default &#39;macro&#39;</span>
<span class="sd"> Strategy to be used for aggregating across mini-batches.</span>
<span class="sd"> &quot;macro&quot;: average the MCC for each batch.</span>
<span class="sd"> &quot;micro&quot;: compute a single MCC across all batches.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; # In this example the network almost always predicts positive</span>
<span class="sd"> &gt;&gt;&gt; false_positives = 1000</span>
<span class="sd"> &gt;&gt;&gt; false_negatives = 1</span>
<span class="sd"> &gt;&gt;&gt; true_positives = 10000</span>
<span class="sd"> &gt;&gt;&gt; true_negatives = 1</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array(</span>
<span class="sd"> [[.3, .7]]*false_positives +</span>
<span class="sd"> [[.7, .3]]*true_negatives +</span>
<span class="sd"> [[.7, .3]]*false_negatives +</span>
<span class="sd"> [[.3, .7]]*true_positives</span>
<span class="sd"> )]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array(</span>
<span class="sd"> [0.]*(false_positives + true_negatives) +</span>
<span class="sd"> [1.]*(false_negatives + true_positives)</span>
<span class="sd"> )]</span>
<span class="sd"> &gt;&gt;&gt; f1 = mx.metric.F1()</span>
<span class="sd"> &gt;&gt;&gt; f1.update(preds = predicts, labels = labels)</span>
<span class="sd"> &gt;&gt;&gt; mcc = mx.metric.MCC()</span>
<span class="sd"> &gt;&gt;&gt; mcc.update(preds = predicts, labels = labels)</span>
<span class="sd"> &gt;&gt;&gt; print f1.get()</span>
<span class="sd"> (&#39;f1&#39;, 0.95233560306652054)</span>
<span class="sd"> &gt;&gt;&gt; print mcc.get()</span>
<span class="sd"> (&#39;mcc&#39;, 0.01917751877733392)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;mcc&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">&quot;macro&quot;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_average</span> <span class="o">=</span> <span class="n">average</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span> <span class="o">=</span> <span class="n">_BinaryClassificationMetrics</span><span class="p">()</span>
<span class="n">EvalMetric</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<div class="viewcode-block" id="MCC.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.MCC.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">update_binary_stats</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_average</span> <span class="o">==</span> <span class="s2">&quot;macro&quot;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">matthewscc</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">matthewscc</span><span class="p">(</span><span class="n">use_global</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">reset_stats</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">matthewscc</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">total_examples</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">matthewscc</span><span class="p">(</span><span class="n">use_global</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">*</span> \
<span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">global_total_examples</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">total_examples</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">global_total_examples</span></div>
<div class="viewcode-block" id="MCC.reset"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.MCC.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the internal evaluation result to initial state.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">reset_stats</span><span class="p">()</span></div>
<div class="viewcode-block" id="MCC.reset_local"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.MCC.reset_local">[docs]</a> <span class="k">def</span> <span class="nf">reset_local</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the internal evaluation result to initial state.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_metrics</span><span class="o">.</span><span class="n">local_reset_stats</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="Perplexity"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Perplexity">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">Perplexity</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes perplexity.</span>
<span class="sd"> Perplexity is a measurement of how well a probability distribution</span>
<span class="sd"> or model predicts a sample. A low perplexity indicates the model</span>
<span class="sd"> is good at predicting the sample.</span>
<span class="sd"> The perplexity of a model q is defined as</span>
<span class="sd"> .. math::</span>
<span class="sd"> b^{\\big(-\\frac{1}{N} \\sum_{i=1}^N \\log_b q(x_i) \\big)}</span>
<span class="sd"> = \\exp \\big(-\\frac{1}{N} \\sum_{i=1}^N \\log q(x_i)\\big)</span>
<span class="sd"> where we let `b = e`.</span>
<span class="sd"> :math:`q(x_i)` is the predicted value of its ground truth</span>
<span class="sd"> label on sample :math:`x_i`.</span>
<span class="sd"> For example, we have three samples :math:`x_1, x_2, x_3` and their labels</span>
<span class="sd"> are :math:`[0, 1, 1]`.</span>
<span class="sd"> Suppose our model predicts :math:`q(x_1) = p(y_1 = 0 | x_1) = 0.3`</span>
<span class="sd"> and :math:`q(x_2) = 1.0`,</span>
<span class="sd"> :math:`q(x_3) = 0.6`. The perplexity of model q is</span>
<span class="sd"> :math:`exp\\big(-(\\log 0.3 + \\log 1.0 + \\log 0.6) / 3\\big) = 1.77109762852`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> ignore_label : int or None</span>
<span class="sd"> Index of invalid label to ignore when</span>
<span class="sd"> counting. By default, sets to -1.</span>
<span class="sd"> If set to `None`, it will include all entries.</span>
<span class="sd"> axis : int (default -1)</span>
<span class="sd"> The axis from prediction that was used to</span>
<span class="sd"> compute softmax. By default use the last</span>
<span class="sd"> axis.</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array([0, 1, 1])]</span>
<span class="sd"> &gt;&gt;&gt; perp = mx.metric.Perplexity(ignore_label=None)</span>
<span class="sd"> &gt;&gt;&gt; perp.update(labels, predicts)</span>
<span class="sd"> &gt;&gt;&gt; print perp.get()</span>
<span class="sd"> (&#39;Perplexity&#39;, 1.7710976285155853)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ignore_label</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;perplexity&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Perplexity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">ignore_label</span><span class="o">=</span><span class="n">ignore_label</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ignore_label</span> <span class="o">=</span> <span class="n">ignore_label</span>
<span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="o">=</span> <span class="n">axis</span>
<div class="viewcode-block" id="Perplexity.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Perplexity.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">num</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">label</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="n">pred</span><span class="o">.</span><span class="n">size</span><span class="o">/</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> \
<span class="s2">&quot;shape mismatch: </span><span class="si">%s</span><span class="s2"> vs. </span><span class="si">%s</span><span class="s2">&quot;</span><span class="o">%</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">context</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">label</span><span class="o">.</span><span class="n">size</span><span class="p">,))</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">pick</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ignore_label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">ignore</span> <span class="o">=</span> <span class="p">(</span><span class="n">label</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">ignore_label</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">num</span> <span class="o">-=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">ignore</span><span class="p">)</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">ignore</span><span class="p">)</span> <span class="o">+</span> <span class="n">ignore</span>
<span class="n">loss</span> <span class="o">-=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">ndarray</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">ndarray</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mf">1e-10</span><span class="p">,</span> <span class="n">pred</span><span class="p">)))</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span>
<span class="n">num</span> <span class="o">+=</span> <span class="n">pred</span><span class="o">.</span><span class="n">size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">loss</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">loss</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="n">num</span></div>
<div class="viewcode-block" id="Perplexity.get"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Perplexity.get">[docs]</a> <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns the current evaluation result.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> Tuple of (str, float)</span>
<span class="sd"> Representing name of the metric and evaluation result.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span><span class="p">))</span></div>
<div class="viewcode-block" id="Perplexity.get_global"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Perplexity.get_global">[docs]</a> <span class="k">def</span> <span class="nf">get_global</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns the current global evaluation result.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> Tuple of (str, float)</span>
<span class="sd"> Representing name of the metric and evaluation result.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span><span class="p">))</span></div></div>
<span class="c1">####################</span>
<span class="c1"># REGRESSION METRICS</span>
<span class="c1">####################</span>
<div class="viewcode-block" id="MAE"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.MAE">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">MAE</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes Mean Absolute Error (MAE) loss.</span>
<span class="sd"> The mean absolute error is given by</span>
<span class="sd"> .. math::</span>
<span class="sd"> \\frac{\\sum_i^n |y_i - \\hat{y}_i|}{n}</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))]</span>
<span class="sd"> &gt;&gt;&gt; mean_absolute_error = mx.metric.MAE()</span>
<span class="sd"> &gt;&gt;&gt; mean_absolute_error.update(labels = labels, preds = predicts)</span>
<span class="sd"> &gt;&gt;&gt; print mean_absolute_error.get()</span>
<span class="sd"> (&#39;mae&#39;, 0.5)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MAE</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<div class="viewcode-block" id="MAE.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.MAE.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</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="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</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">mae</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">label</span> <span class="o">-</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">mae</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">mae</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span> <span class="c1"># numpy.prod(label.shape)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="mi">1</span> <span class="c1"># numpy.prod(label.shape)</span></div></div>
<div class="viewcode-block" id="MSE"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.MSE">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">MSE</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes Mean Squared Error (MSE) loss.</span>
<span class="sd"> The mean squared error is given by</span>
<span class="sd"> .. math::</span>
<span class="sd"> \\frac{\\sum_i^n (y_i - \\hat{y}_i)^2}{n}</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))]</span>
<span class="sd"> &gt;&gt;&gt; mean_squared_error = mx.metric.MSE()</span>
<span class="sd"> &gt;&gt;&gt; mean_squared_error.update(labels = labels, preds = predicts)</span>
<span class="sd"> &gt;&gt;&gt; print mean_squared_error.get()</span>
<span class="sd"> (&#39;mse&#39;, 0.375)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;mse&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MSE</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<div class="viewcode-block" id="MSE.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.MSE.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</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="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</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">mse</span> <span class="o">=</span> <span class="p">((</span><span class="n">label</span> <span class="o">-</span> <span class="n">pred</span><span class="p">)</span><span class="o">**</span><span class="mf">2.0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">mse</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">mse</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span> <span class="c1"># numpy.prod(label.shape)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="mi">1</span> <span class="c1"># numpy.prod(label.shape)</span></div></div>
<div class="viewcode-block" id="RMSE"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.RMSE">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">RMSE</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes Root Mean Squred Error (RMSE) loss.</span>
<span class="sd"> The root mean squared error is given by</span>
<span class="sd"> .. math::</span>
<span class="sd"> \\sqrt{\\frac{\\sum_i^n (y_i - \\hat{y}_i)^2}{n}}</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))]</span>
<span class="sd"> &gt;&gt;&gt; root_mean_squared_error = mx.metric.RMSE()</span>
<span class="sd"> &gt;&gt;&gt; root_mean_squared_error.update(labels = labels, preds = predicts)</span>
<span class="sd"> &gt;&gt;&gt; print root_mean_squared_error.get()</span>
<span class="sd"> (&#39;rmse&#39;, 0.612372457981)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;rmse&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">RMSE</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<div class="viewcode-block" id="RMSE.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.RMSE.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</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="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">shape</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">rmse</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(((</span><span class="n">label</span> <span class="o">-</span> <span class="n">pred</span><span class="p">)</span><span class="o">**</span><span class="mf">2.0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">rmse</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">rmse</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="mi">1</span></div></div>
<div class="viewcode-block" id="CrossEntropy"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CrossEntropy">[docs]</a><span class="nd">@register</span>
<span class="nd">@alias</span><span class="p">(</span><span class="s1">&#39;ce&#39;</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">CrossEntropy</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes Cross Entropy loss.</span>
<span class="sd"> The cross entropy over a batch of sample size :math:`N` is given by</span>
<span class="sd"> .. math::</span>
<span class="sd"> -\\sum_{n=1}^{N}\\sum_{k=1}^{K}t_{nk}\\log (y_{nk}),</span>
<span class="sd"> where :math:`t_{nk}=1` if and only if sample :math:`n` belongs to class :math:`k`.</span>
<span class="sd"> :math:`y_{nk}` denotes the probability of sample :math:`n` belonging to</span>
<span class="sd"> class :math:`k`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> eps : float</span>
<span class="sd"> Cross Entropy loss is undefined for predicted value is 0 or 1,</span>
<span class="sd"> so predicted values are added with the small constant.</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array([0, 1, 1])]</span>
<span class="sd"> &gt;&gt;&gt; ce = mx.metric.CrossEntropy()</span>
<span class="sd"> &gt;&gt;&gt; ce.update(labels, predicts)</span>
<span class="sd"> &gt;&gt;&gt; print ce.get()</span>
<span class="sd"> (&#39;cross-entropy&#39;, 0.57159948348999023)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-12</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;cross-entropy&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CrossEntropy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
<div class="viewcode-block" id="CrossEntropy.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CrossEntropy.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">assert</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">prob</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">int64</span><span class="p">(</span><span class="n">label</span><span class="p">)]</span>
<span class="n">cross_entropy</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prob</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">cross_entropy</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">cross_entropy</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div></div>
<div class="viewcode-block" id="NegativeLogLikelihood"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.NegativeLogLikelihood">[docs]</a><span class="nd">@register</span>
<span class="nd">@alias</span><span class="p">(</span><span class="s1">&#39;nll_loss&#39;</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">NegativeLogLikelihood</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes the negative log-likelihood loss.</span>
<span class="sd"> The negative log-likelihoodd loss over a batch of sample size :math:`N` is given by</span>
<span class="sd"> .. math::</span>
<span class="sd"> -\\sum_{n=1}^{N}\\sum_{k=1}^{K}t_{nk}\\log (y_{nk}),</span>
<span class="sd"> where :math:`K` is the number of classes, :math:`y_{nk}` is the prediceted probability for</span>
<span class="sd"> :math:`k`-th class for :math:`n`-th sample. :math:`t_{nk}=1` if and only if sample</span>
<span class="sd"> :math:`n` belongs to class :math:`k`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> eps : float</span>
<span class="sd"> Negative log-likelihood loss is undefined for predicted value is 0,</span>
<span class="sd"> so predicted values are added with the small constant.</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array([0, 1, 1])]</span>
<span class="sd"> &gt;&gt;&gt; nll_loss = mx.metric.NegativeLogLikelihood()</span>
<span class="sd"> &gt;&gt;&gt; nll_loss.update(labels, predicts)</span>
<span class="sd"> &gt;&gt;&gt; print nll_loss.get()</span>
<span class="sd"> (&#39;nll-loss&#39;, 0.57159948348999023)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-12</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;nll-loss&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">NegativeLogLikelihood</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
<div class="viewcode-block" id="NegativeLogLikelihood.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.NegativeLogLikelihood.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="n">num_examples</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">assert</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">num_examples</span><span class="p">,</span> <span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">num_examples</span><span class="p">)</span>
<span class="n">prob</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_examples</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int64</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">int64</span><span class="p">(</span><span class="n">label</span><span class="p">)]</span>
<span class="n">nll</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prob</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">nll</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">nll</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num_examples</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="n">num_examples</span></div></div>
<div class="viewcode-block" id="PearsonCorrelation"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.PearsonCorrelation">[docs]</a><span class="nd">@register</span>
<span class="nd">@alias</span><span class="p">(</span><span class="s1">&#39;pearsonr&#39;</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">PearsonCorrelation</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes Pearson correlation.</span>
<span class="sd"> The pearson correlation is given by</span>
<span class="sd"> .. math::</span>
<span class="sd"> \\frac{cov(y, \\hat{y})}{\\sigma{y}\\sigma{\\hat{y}}}</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> average : str, default &#39;macro&#39;</span>
<span class="sd"> Strategy to be used for aggregating across mini-batches.</span>
<span class="sd"> &quot;macro&quot;: average the pearsonr scores for each batch.</span>
<span class="sd"> &quot;micro&quot;: compute a single pearsonr score across all batches.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array([[1, 0], [0, 1], [0, 1]])]</span>
<span class="sd"> &gt;&gt;&gt; pr = mx.metric.PearsonCorrelation()</span>
<span class="sd"> &gt;&gt;&gt; pr.update(labels, predicts)</span>
<span class="sd"> &gt;&gt;&gt; print pr.get()</span>
<span class="sd"> (&#39;pearsonr&#39;, 0.42163704544016178)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;pearsonr&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">=</span> <span class="n">average</span>
<span class="nb">super</span><span class="p">(</span><span class="n">PearsonCorrelation</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">==</span> <span class="s1">&#39;micro&#39;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset_micro</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">reset_micro</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sse_p</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_mean_p</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sse_l</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_mean_l</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_pred_nums</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label_nums</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conv</span> <span class="o">=</span> <span class="mi">0</span>
<div class="viewcode-block" id="PearsonCorrelation.reset"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.PearsonCorrelation.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">==</span> <span class="s1">&#39;micro&#39;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset_micro</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">update_variance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_values</span><span class="p">,</span> <span class="o">*</span><span class="n">aggregate</span><span class="p">):</span>
<span class="c1">#Welford&#39;s online algorithm for variance update</span>
<span class="n">count</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">m_2</span> <span class="o">=</span> <span class="n">aggregate</span>
<span class="n">count</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_values</span><span class="p">)</span>
<span class="n">delta</span> <span class="o">=</span> <span class="n">new_values</span> <span class="o">-</span> <span class="n">mean</span>
<span class="n">mean</span> <span class="o">+=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">delta</span> <span class="o">/</span> <span class="n">count</span><span class="p">)</span>
<span class="n">delta_2</span> <span class="o">=</span> <span class="n">new_values</span> <span class="o">-</span> <span class="n">mean</span>
<span class="n">m_2</span> <span class="o">+=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">delta</span> <span class="o">*</span> <span class="n">delta_2</span><span class="p">)</span>
<span class="k">return</span> <span class="n">count</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">m_2</span>
<span class="k">def</span> <span class="nf">update_cov</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_conv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv</span> <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">label</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_l</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">pred</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_p</span><span class="p">))</span>
<div class="viewcode-block" id="PearsonCorrelation.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.PearsonCorrelation.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="n">check_label_shapes</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">==</span> <span class="s1">&#39;macro&#39;</span><span class="p">:</span>
<span class="n">pearson_corr</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">label</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="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">pearson_corr</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">pearson_corr</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label_nums</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_l</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sse_l</span> <span class="o">=</span> \
<span class="bp">self</span><span class="o">.</span><span class="n">update_variance</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_nums</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_l</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sse_l</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_cov</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_pred_nums</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sse_p</span> <span class="o">=</span> \
<span class="bp">self</span><span class="o">.</span><span class="n">update_variance</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred_nums</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sse_p</span><span class="p">)</span></div>
<div class="viewcode-block" id="PearsonCorrelation.get"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.PearsonCorrelation.get">[docs]</a> <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">average</span> <span class="o">==</span> <span class="s1">&#39;macro&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">n</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_nums</span>
<span class="n">pearsonr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv</span> <span class="o">/</span> <span class="p">((</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sse_p</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_sse_l</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)))</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">pearsonr</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="PCC"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.PCC">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">PCC</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;PCC is a multiclass equivalent for the Matthews correlation coefficient derived</span>
<span class="sd"> from a discrete solution to the Pearson correlation coefficient.</span>
<span class="sd"> .. math::</span>
<span class="sd"> \\text{PCC} = \\frac {\\sum _{k}\\sum _{l}\\sum _{m}C_{kk}C_{lm}-C_{kl}C_{mk}}</span>
<span class="sd"> {{\\sqrt {\\sum _{k}(\\sum _{l}C_{kl})(\\sum _{k&#39;|k&#39;\\neq k}\\sum _{l&#39;}C_{k&#39;l&#39;})}}</span>
<span class="sd"> {\\sqrt {\\sum _{k}(\\sum _{l}C_{lk})(\\sum _{k&#39;|k&#39;\\neq k}\\sum _{l&#39;}C_{l&#39;k&#39;})}}}</span>
<span class="sd"> defined in terms of a K x K confusion matrix C.</span>
<span class="sd"> When there are more than two labels the PCC will no longer range between -1 and +1.</span>
<span class="sd"> Instead the minimum value will be between -1 and 0 depending on the true distribution.</span>
<span class="sd"> The maximum value is always +1.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; # In this example the network almost always predicts positive</span>
<span class="sd"> &gt;&gt;&gt; false_positives = 1000</span>
<span class="sd"> &gt;&gt;&gt; false_negatives = 1</span>
<span class="sd"> &gt;&gt;&gt; true_positives = 10000</span>
<span class="sd"> &gt;&gt;&gt; true_negatives = 1</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array(</span>
<span class="sd"> [[.3, .7]]*false_positives +</span>
<span class="sd"> [[.7, .3]]*true_negatives +</span>
<span class="sd"> [[.7, .3]]*false_negatives +</span>
<span class="sd"> [[.3, .7]]*true_positives</span>
<span class="sd"> )]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array(</span>
<span class="sd"> [0]*(false_positives + true_negatives) +</span>
<span class="sd"> [1]*(false_negatives + true_positives)</span>
<span class="sd"> )]</span>
<span class="sd"> &gt;&gt;&gt; f1 = mx.metric.F1()</span>
<span class="sd"> &gt;&gt;&gt; f1.update(preds = predicts, labels = labels)</span>
<span class="sd"> &gt;&gt;&gt; pcc = mx.metric.PCC()</span>
<span class="sd"> &gt;&gt;&gt; pcc.update(preds = predicts, labels = labels)</span>
<span class="sd"> &gt;&gt;&gt; print f1.get()</span>
<span class="sd"> (&#39;f1&#39;, 0.95233560306652054)</span>
<span class="sd"> &gt;&gt;&gt; print pcc.get()</span>
<span class="sd"> (&#39;pcc&#39;, 0.01917751877733392)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;pcc&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="mi">2</span>
<span class="nb">super</span><span class="p">(</span><span class="n">PCC</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="n">has_global_stats</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_grow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inc</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lcm</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lcm</span><span class="p">,</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="n">inc</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">inc</span><span class="p">)),</span> <span class="s1">&#39;constant&#39;</span><span class="p">,</span> <span class="n">constant_values</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">gcm</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">gcm</span><span class="p">,</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="n">inc</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">inc</span><span class="p">)),</span> <span class="s1">&#39;constant&#39;</span><span class="p">,</span> <span class="n">constant_values</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">+=</span> <span class="n">inc</span>
<span class="k">def</span> <span class="nf">_calc_mcc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cmat</span><span class="p">):</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">cmat</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">cmat</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">cmat</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">cov_xx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="n">x</span><span class="p">))</span>
<span class="n">cov_yy</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="n">y</span><span class="p">))</span>
<span class="k">if</span> <span class="n">cov_xx</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">cov_yy</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">)</span>
<span class="n">i</span> <span class="o">=</span> <span class="n">cmat</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span>
<span class="n">cov_xy</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">i</span> <span class="o">*</span> <span class="n">n</span> <span class="o">-</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cov_xy</span> <span class="o">/</span> <span class="p">(</span><span class="n">cov_xx</span> <span class="o">*</span> <span class="n">cov_yy</span><span class="p">)</span> <span class="o">**</span> <span class="mf">0.5</span>
<div class="viewcode-block" id="PCC.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.PCC.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="c1"># update the confusion matrix</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="k">if</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">label</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="k">if</span> <span class="n">n</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grow</span><span class="p">(</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
<span class="n">bcm</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span>
<span class="n">bcm</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lcm</span> <span class="o">+=</span> <span class="n">bcm</span>
<span class="bp">self</span><span class="o">.</span><span class="n">gcm</span> <span class="o">+=</span> <span class="n">bcm</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="mi">1</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">sum_metric</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calc_mcc</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lcm</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">global_sum_metric</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calc_mcc</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gcm</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span>
<div class="viewcode-block" id="PCC.reset"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.PCC.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the internal evaluation result to initial state.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">gcm</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset_local</span><span class="p">()</span></div>
<div class="viewcode-block" id="PCC.reset_local"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.PCC.reset_local">[docs]</a> <span class="k">def</span> <span class="nf">reset_local</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Resets the local portion of the internal evaluation results to initial state.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lcm</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">))</span></div></div>
<div class="viewcode-block" id="Loss"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Loss">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">Loss</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Dummy metric for directly printing loss.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;loss&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Loss</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<div class="viewcode-block" id="Loss.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Loss.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
<span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">preds</span><span class="p">]</span>
<span class="k">for</span> <span class="n">pred</span> <span class="ow">in</span> <span class="n">preds</span><span class="p">:</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">loss</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">loss</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">pred</span><span class="o">.</span><span class="n">size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="n">pred</span><span class="o">.</span><span class="n">size</span></div></div>
<div class="viewcode-block" id="Torch"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Torch">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">Torch</span><span class="p">(</span><span class="n">Loss</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Dummy metric for torch criterions.&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;torch&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Torch</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span></div>
<div class="viewcode-block" id="Caffe"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.Caffe">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">Caffe</span><span class="p">(</span><span class="n">Loss</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Dummy metric for caffe criterions.&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;caffe&#39;</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Caffe</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">)</span></div>
<div class="viewcode-block" id="CustomMetric"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CustomMetric">[docs]</a><span class="nd">@register</span>
<span class="k">class</span> <span class="nc">CustomMetric</span><span class="p">(</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes a customized evaluation metric.</span>
<span class="sd"> The `feval` function can return a `tuple` of (sum_metric, num_inst) or return</span>
<span class="sd"> an `int` sum_metric.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> feval : callable(label, pred)</span>
<span class="sd"> Customized evaluation function.</span>
<span class="sd"> name : str, optional</span>
<span class="sd"> The name of the metric. (the default is None).</span>
<span class="sd"> allow_extra_outputs : bool, optional</span>
<span class="sd"> If true, the prediction outputs can have extra outputs.</span>
<span class="sd"> This is useful in RNN, where the states are also produced</span>
<span class="sd"> in outputs for forwarding. (the default is False).</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this metric instance for display.</span>
<span class="sd"> output_names : list of str, or None</span>
<span class="sd"> Name of predictions that should be used when updating with update_dict.</span>
<span class="sd"> By default include all predictions.</span>
<span class="sd"> label_names : list of str, or None</span>
<span class="sd"> Name of labels that should be used when updating with update_dict.</span>
<span class="sd"> By default include all labels.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))]</span>
<span class="sd"> &gt;&gt;&gt; labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))]</span>
<span class="sd"> &gt;&gt;&gt; feval = lambda x, y : (x + y).mean()</span>
<span class="sd"> &gt;&gt;&gt; eval_metrics = mx.metric.CustomMetric(feval=feval)</span>
<span class="sd"> &gt;&gt;&gt; eval_metrics.update(labels, predicts)</span>
<span class="sd"> &gt;&gt;&gt; print eval_metrics.get()</span>
<span class="sd"> (&#39;custom(&lt;lambda&gt;)&#39;, 6.0)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feval</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_extra_outputs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">feval</span><span class="o">.</span><span class="vm">__name__</span>
<span class="k">if</span> <span class="n">name</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;&lt;&#39;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;custom(</span><span class="si">%s</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="n">name</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CustomMetric</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">feval</span><span class="o">=</span><span class="n">feval</span><span class="p">,</span>
<span class="n">allow_extra_outputs</span><span class="o">=</span><span class="n">allow_extra_outputs</span><span class="p">,</span>
<span class="n">output_names</span><span class="o">=</span><span class="n">output_names</span><span class="p">,</span> <span class="n">label_names</span><span class="o">=</span><span class="n">label_names</span><span class="p">,</span>
<span class="n">has_global_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_feval</span> <span class="o">=</span> <span class="n">feval</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_allow_extra_outputs</span> <span class="o">=</span> <span class="n">allow_extra_outputs</span>
<div class="viewcode-block" id="CustomMetric.update"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CustomMetric.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates the internal evaluation result.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> labels : list of `NDArray`</span>
<span class="sd"> The labels of the data.</span>
<span class="sd"> preds : list of `NDArray`</span>
<span class="sd"> Predicted values.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_allow_extra_outputs</span><span class="p">:</span>
<span class="n">labels</span><span class="p">,</span> <span class="n">preds</span> <span class="o">=</span> <span class="n">check_label_shapes</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">pred</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="n">reval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_feval</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reval</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
<span class="p">(</span><span class="n">sum_metric</span><span class="p">,</span> <span class="n">num_inst</span><span class="p">)</span> <span class="o">=</span> <span class="n">reval</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">sum_metric</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">sum_metric</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="n">num_inst</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="n">num_inst</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum_metric</span> <span class="o">+=</span> <span class="n">reval</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_sum_metric</span> <span class="o">+=</span> <span class="n">reval</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_inst</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_num_inst</span> <span class="o">+=</span> <span class="mi">1</span></div>
<div class="viewcode-block" id="CustomMetric.get_config"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.CustomMetric.get_config">[docs]</a> <span class="k">def</span> <span class="nf">get_config</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;CustomMetric cannot be serialized&quot;</span><span class="p">)</span></div></div>
<span class="c1"># pylint: disable=invalid-name</span>
<div class="viewcode-block" id="np"><a class="viewcode-back" href="../../api/metric/index.html#mxnet.metric.np">[docs]</a><span class="k">def</span> <span class="nf">np</span><span class="p">(</span><span class="n">numpy_feval</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_extra_outputs</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Creates a custom evaluation metric that receives its inputs as numpy arrays.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> numpy_feval : callable(label, pred)</span>
<span class="sd"> Custom evaluation function that receives labels and predictions for a minibatch</span>
<span class="sd"> as numpy arrays and returns the corresponding custom metric as a floating point number.</span>
<span class="sd"> name : str, optional</span>
<span class="sd"> Name of the custom metric.</span>
<span class="sd"> allow_extra_outputs : bool, optional</span>
<span class="sd"> Whether prediction output is allowed to have extra outputs. This is useful in cases</span>
<span class="sd"> like RNN where states are also part of output which can then be fed back to the RNN</span>
<span class="sd"> in the next step. By default, extra outputs are not allowed.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> float</span>
<span class="sd"> Custom metric corresponding to the provided labels and predictions.</span>
<span class="sd"> Example</span>
<span class="sd"> -------</span>
<span class="sd"> &gt;&gt;&gt; def custom_metric(label, pred):</span>
<span class="sd"> ... return np.mean(np.abs(label-pred))</span>
<span class="sd"> ...</span>
<span class="sd"> &gt;&gt;&gt; metric = mx.metric.np(custom_metric)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">feval</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Internal eval function.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">numpy_feval</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="n">feval</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="n">numpy_feval</span><span class="o">.</span><span class="vm">__name__</span>
<span class="k">return</span> <span class="n">CustomMetric</span><span class="p">(</span><span class="n">feval</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">allow_extra_outputs</span><span class="p">)</span></div>
<span class="c1"># pylint: enable=invalid-name</span>
</pre></div>
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