Release Highlights for tttrlib 0.24

We are pleased to announce the release of tttrlib 0.24, which comes with many bug fixes and new features! We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes.

To install the latest version with conda:

conda install -c tpeulen tttrlib

Test for plotting

The inspection.permutation_importance can be used to get an estimate of the importance of each feature, for any fitted estimator:

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
x = np.linspace(0, 5)
ax.plot(x, np.sin(x))
ax.set_title("Permutation Importance of each feature")
ax.set_ylabel("Features")
fig.tight_layout()
plt.show()

Improved PTU header support

The ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor now have native support for missing values (NaNs). This means that there is no need for imputing data when training or predicting.

print("Better Header support")

Better Header support

Writing of TTTR data

Most estimators based on nearest neighbors graphs now accept precomputed sparse graphs as input, to reuse the same graph for multiple estimator fits. To use this feature in a pipeline, one can use the memory parameter, along with one of the two new transformers, neighbors.KNeighborsTransformer and neighbors.RadiusNeighborsTransformer. The precomputation can also be performed by custom estimators to use alternative implementations, such as approximate nearest neighbors methods. See more details in the User Guide.

from tempfile import TemporaryDirectory
import tttrlib


# with TemporaryDirectory(prefix="tttrlib_temp_") as tmpdir:
#     estimator = make_pipeline(
#         KNeighborsTransformer(n_neighbors=10, mode='distance'),
#         Isomap(n_neighbors=10, metric='precomputed'),
#         memory=tmpdir)
#     estimator.fit(X)
#
#     # We can decrease the number of neighbors and the graph will not be
#     # recomputed.
#     estimator.set_params(isomap__n_neighbors=5)
#     estimator.fit(X)