Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Seglearn: A Python Package for Learning Sequences and Time Series
Authors: David M. Burns, Cari M. Whyne
JMLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance comparison was conducted using our human activity recognition data set with 140 multivariate time series with 6 channels sampled uniformly at 50 Hz and 7 activity classes. Classification accuracy was measured on 35 series held out for testing, and 105 used for training. seglearn, cesium-ml, and tsfresh were tested using the sklearn implementation of the SVM classifier... Classification accuracy was identical between cesium-ml, tsfresh, and seglearn... though seglearn significantly outperformed the other packages in terms of computation time. (Table 2: Comparison of time series learning package performance...) |
| Researcher Affiliation | Academia | David M. Burns EMAIL Sunnybrook Research Institute Cari M. Whyne EMAIL Sunnybrook Research Institute 2075 Bayview Ave. Room S620. Toronto, ON, Canada. M4N 3M5. |
| Pseudocode | No | The paper contains Figure 1 which shows "Example seglearn pipelines" with block diagrams and an example Python code snippet in Section 5. It does not contain any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | seglearn is an open-source Python package for performing machine learning on time series or sequences. ... Source code and documentation can be downloaded from https://github.com/dmbee/seglearn. |
| Open Datasets | Yes | A human activity recognition data set (Burns et al., 2018) consisting of inertial sensor data recorded by a smartwatch worn during shoulder rehabilitation exercises is provided with the source code to demonstrate the features and usage of the seglearn package. |
| Dataset Splits | Yes | Classification accuracy was measured on 35 series held out for testing, and 105 used for training. |
| Hardware Specification | Yes | The testing was performed using an Intel Core i7-4770 testbed with 16 GB of installed memory, on Linux Mint 18.3 with Python 2.7.12. |
| Software Dependencies | Yes | The testing was performed using an Intel Core i7-4770 testbed with 16 GB of installed memory, on Linux Mint 18.3 with Python 2.7.12. tslearn v0.1.18.4, cesium-ml v0.9.6, tsfresh v0.11.1 and seglearn v1.0.2. |
| Experiment Setup | Yes | Classification performance of the global alignment kernel SVM (GAK-SVM) implemented in tslearn was poor on our data set, even following hyper-parameter optimization of gamma by grid search over the log space [10 4, 104]. |