Release Notes

0.7.4 Fix the problem that sample_FP_pair and sample_FP_pair_deterministic may create FP pairs on NN.

0.7.3

Fix the problem when the sample size is smaller than the number of neighbors, PaCMAP will crash without proper error report. Now the PaCMAP will reduce the number of neighbors and generate a warning message.

0.7.2

Fix the problem in using user-provided initialization, as well as discrepancy between documentation and code in default n_neighbors parameter.

0.7.1

Update the required numba version to numba >= 0.57.

0.7.0

Now officially supports the save and load methods. pacmap.save(reducer, common_prefix) will save the PaCMAP instance (and the AnnoyIndex if save_tree=True) to the location specified by the common_prefix. The PaCMAP instance will be named as {common_prefix}.pkl and the Annoy Index will be named as {common_prefix}.ann. Similarly, pacmap.load(common_prefix) loads the saved PaCMAP instance.

0.6.0

Now officially supports the transform feature. The transform operation is useful for projecting a new dataset into an existing embedded space. In the current version of implementation, the transform method will treat the input as an additional dataset, which means the same point could be mapped into a different place.

0.5.0

Now support setting random_state when creating pacmap.PaCMAP instances for better reproducibility.

Fix the default initialization to PCA to resolve inconsistency between code and description.

Setting the random_state will affect the numpy random seed in your local environment. However, you may still get different results even if the random_state parameter is set to be the same. This is because numba parallelization makes some of the functions undeterministic. That being said, fixing the random state will always give you the same set of pairs and initialization, which ensure the difference is minimal.

0.4.1

Now the default value for n_neighbors is 10. To enable automatic parameter selection, please set it to None.

0.4

Now supports user-specified nearest neighbor pairs. See section How to use user-specified nearest neighbor below.

The fit function and the fit_transform function now has an extra parameter save_pairs that decides whether the pairs sampled in this run will be saved to save time for reproducing experiments with other hyperparameters (default to True).

0.3

Now supports user-specified matrix as initialization through init parameter. The matrix must be an numpy ndarray with the shape (N, 2).

0.2

Adding adaptive default value for n_neighbors: for large datasets with sample size N > 10000, the default value will be set to 10 + 15 * (log10(N) - 4), rounding to the nearest integer.

0.1

Initial Release