Toolbox for Multimodal Learn (scikit-multimodallearn)
Authors: Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette
JMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1 shows the results of a benchmark, with 5-folds cross validation on the multi-view version of MNist presented in Section 2.2. We tested a mono-view decision tree and Adaboost (Schapire, 2013) on each view, their early fusion versions and the four algorithms of scikit-multimodallearn. All the implemented algorithms output higher accuracy scores than the mono-view approaches and the naive fusion methods. Mu Combo, despite being dedicated to imbalance problems, still displays an interesting score for this balanced task. |
| Researcher Affiliation | Academia | Dominique Benielli firstname.lastname[at]univ-amu.fr C ecile Capponi firstname.lastname[at]lis-lab.fr Sokol Ko co firstname.lastname[at]mines-stetienne.fr Hachem Kadri firstname.lastname[at]lis-lab.fr Riikka Huusari firstname.lastname[at]lis-lab.fr Department of Computer Science Aix-Marseille University, CNRS, LIS, 13013 Marseille, France Baptiste Bauvin firstname.lastname[at]lis-lab.fr Fran cois Laviolette firstname.lastname[at]ift.ulaval.ca Pavillon Adrien-Pouliot, Local PLT-3908, 1065, av. de la M edecine, Universit e Laval, Qu ebec (QC) G1V 0A6, Canada |
| Pseudocode | No | The paper describes algorithms (MVML, lp-MKL, Mumbo, Mu Com Bo) in text and provides code snippets for library usage in Python, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | scikit-multimodallearn is a Python library for multimodal supervised learning, licensed under Free BSD, and compatible with the well-known scikit-learn toolbox (Pedregosa et al., 2011). If needed, the package can be downloaded from Github.2 The development has been performed using continuous integration with Docker and automated tests, covering 90% of the code. Footnote 2: Hosted here https://github.com/dbenielli/scikit-multimodallearn . |
| Open Datasets | Yes | In the following example, a multiview data set is instantiated with a multi-view version of MNist (Deng, 2012) for which we generated the HOG (Triggs and Dalal, 2005) in 12 directions, and selected 3 random directions for each view. |
| Dataset Splits | Yes | cross val score can be used transparently on multimodal estimator for multiviews data. >>> # usage cross_val_score on Mu Combo Classifier >>> cross_val_score(clfm , XX , y, cv=5) array ([0.96666667 , 0.96666667 , 0.9, 0.93333333 , 1. ]) Figure 1 shows the results of a benchmark, with 5-folds cross validation on the multi-view version of MNist presented in Section 2.2. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software dependencies like "scikit-learn" and "numpy" but does not specify their version numbers. For example, Section 1 states: "Compatible with scikit-learn and numpy libraries". |
| Experiment Setup | Yes | >>> base_estimator = Decision Tree Classifier (max_depth =4) >>> clf = Mumbo Classifier ( base_estimator =base_estimator , n_estimators =4, random_state =7) >>> est = One Vs One Classifier (MVML(lmbda =0.1 , eta=1, nystrom_param =0.2)) >>> param = { estimator__learn_A : (1, 3), estimator__learn_w : (0, 1), estimator__n_loops : (6, 10), estimator__nystrom_param : (1.0 , 0.3) , estimator__kernel : ( linear , polynomial ) , estimator__lmbda : (0.1 ,), estimator__eta : (1,)} |