pystruct - Learning Structured Prediction in Python
Authors: Andreas C. Müller, Sven Behnke
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using this simple setup, we achieve an accuracy of 30.3 on the validation set following the protocol of Kr ahenb uhl and Koltun (2012), who report 30.2 using a more complex approach. Training the structured model takes approximately 30 minutes using a single i7 core. [...] Results are shown in Figure 1. We report learning times and accuracy for varying regularization parameter C. The MNIST data set has 60 000 training examples, 784 features and 10 classes. |
| Researcher Affiliation | Academia | Andreas C. M uller EMAIL Sven Behnke EMAIL Institute of Computer Science, Department VI University of Bonn Bonn, Germany |
| Pseudocode | No | The paper provides a code listing (Listing 1) which demonstrates usage of the library's functions rather than presenting a general algorithm or pseudocode block for a method. |
| Open Source Code | Yes | Py Struct is BSD-licensed, allowing modification and redistribution of the code, as well as use in commercial applications. By embracing paradigms established in the scientific Python community and reusing the interface of the widely-used scikit-learn library (Pedregosa et al., 2011), Py Struct can be used in existing projects, replacing standard classifiers. |
| Open Datasets | Yes | We demonstrate how to learn an n-slack support vector machine (Tsochantaridis et al., 2006) on a superpixel-based CRF on the popular Pascal data set. [...] The preprocessed data can be downloaded at http://www.ais.uni-bonn.de/download/datasets.html. [...] We compare performance of the Crammer Singer multi-class SVM with respect to learning time and accuracy on the MNIST data set of handwritten digits. |
| Dataset Splits | Yes | Using this simple setup, we achieve an accuracy of 30.3 on the validation set following the protocol of Kr ahenb uhl and Koltun (2012), who report 30.2 using a more complex approach. |
| Hardware Specification | Yes | Training the structured model takes approximately 30 minutes using a single i7 core. |
| Software Dependencies | Yes | The cutting plane implementation uses the cvxopt package (Andersen et al., 2012) for quadratic optimization. [...] CVXOPT: A Python package for convex optimization, version 1.1.5. |
| Experiment Setup | Yes | Listing 1: Example of defining and learning a CRF model. 1 model = crfs.Edge Feature Graph CRF( 2 class_weight=inverse_frequency, symmetric_edge_features=[0, 1], 3 antisymmetric_edge_features=[2], inference_method= qpbo ) 4 5 ssvm = learners.NSlack SSVM(model, C=0.01, n_jobs=-1) 6 ssvm.fit(X, Y) [...] We report learning times and accuracy for varying regularization parameter C. |