Bibliographic record
Abstract
This release features two new types of lifting functions: radial basis functions, and random Fourier features. Click the links for examples, or check them out on Binder! You can now also use almost any <code>scikit-learn</code> regressor as a backend for EDMD with <code>EdmdMeta</code>. You can find a cool example of sparse regression with the lasso here. Finally, two quality-of-life changes are introduced in this update. You can access your lifting function feature names with <code>KoopmanLiftingFn.get_feature_names_out()</code>, and you can quickly plot Koopman predictions and Koopman operator properties with a bunch of <code>plot_*()</code> methods scattered throughout the library. See below for more details. Note that in this release, we are <strong>dropping official Python 3.7 support</strong>, though almost all features should still work. <strong>Full changelog</strong>: https://github.com/decargroup/pykoop/compare/v1.0.5...v1.1.0 New features Added radial basis function (RBF) lifting functions in <code>RbfLiftingFn</code>, along with several ways to choose centers (https://github.com/decargroup/pykoop/pull/103) Added random Fourier feature (RFF) lifting functions in <code>KernelApproxLiftingFn</code>, along with other kernel approximations (https://github.com/decargroup/pykoop/pull/110) Added constant lifting function in <code>ConstantLiftingFn</code> (https://github.com/decargroup/pykoop/pull/85) Added support for <code>scikit-learn</code> linear regressors in <code>EdmdMeta</code> (https://github.com/decargroup/pykoop/pull/92) Added support for feature name tracking as strings in <code>KoopmanLiftingFn.get_feature_names_in()</code> and <code>KoopmanLiftingFn.get_feature_names_out()</code>. If you pass in a <code>pandas.DataFrame</code>, then <code>pykoop</code> can take the feature names from there (https://github.com/decargroup/pykoop/pull/75) Added easy plotting helpers in <code>KoopmanLiftingFn.plot_lifted_trajectory()</code>, <code>KoopmanRegressor.plot_bode()</code>, <code>KoopmanRegressor.plot_eigenvalues()</code>, <code>KoopmanRegressor.plot_koopman_matrix()</code>, <code>KoopmanRegressor.plot_svd()</code>, <code>KoopmanPipeline.plot_predicted_trajectory()</code>, <code>KoopmanPipeline.plot_bode()</code>, <code>KoopmanPipeline.plot_eigenvalues()</code>, <code>KoopmanPipeline.plot_koopman_matrix()</code>, and <code>KoopmanPipeline.plot_svd()</code> (https://github.com/decargroup/pykoop/pull/83) Added <code>example_data_pendulum()</code> and <code>example_data_duffing()</code>. Bug fixes Fixed bug where <code>predict_trajectory</code> indexing was wrong when <code>relift_state=false</code> (https://github.com/decargroup/pykoop/pull/112) Fixed Binder package versions (https://github.com/decargroup/pykoop/pull/108)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.106 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".