We were using cffi in order to build fewer wheels, but with the new CI setup introduced in #54 being relatively successful, we can try out PyO3 again; as the need to rebuild for every python version exists in the build matrix already.
/usr/local/lib/python3.7/dist-packages/fastrank/fastrank/init.py in ()
4 from .ffi import ffi
5
----> 6 lib = ffi.dlopen(os.path.join(os.path.dirname(file), 'native.so'), 4098)
7 del os
OSError: cannot load library '/usr/local/lib/python3.7/dist-packages/fastrank/fastrank/native.so': /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by /usr/local/lib/python3.7/dist-packages/fastrank/fastrank/native.so)
I've a question regarding running Coordinate Ascent. I noticed from the colab file https://colab.research.google.com/drive/1IjF7yTin1XaNO_6mBNxAoQYTmF0nckk1 that the model is trained using NDCG. Is it possible to train the model using a user-defined metric? Or how can we make the model optimize for different metrics, e.g. MAP or ERR?
Write a wrapper for the library with argparse and friends that matches Ranklib's command-line behavior for these two models (i.e., -ranker 4 is CA, -ranker 8 is RF, all others error.)
DEPRECATION: Python 3.5 reached the end of its life on September 13th, 2020. Please upgrade your Python as Python 3.5 is no longer maintained. pip 21.0 will drop support for Python 3.5 in January 2021. pip 21.0 will remove support for this functionality.
John, I'm not clear on how to make a dataset. E.g. I have a dataframe with 3 columns: "qid", "features", "label" with types string, np.array and int, how do I make it into a dataset?