data-mining

Summary

mypy shows some issues in LightGBM's Python package.

mypy \
    --exclude='python-package/compile/|python-package/build' \
    --ignore-missing-imports \
    python-package/

python-package/lightgbm/compat.py:12: error: Name 'Series' already defined (possibly by an import)
python-package

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

I am working on creating a WandbCallback for Weights and Biases. I am glad that CatBoost has a callback system in place but it would be great if we can extend the interface.

The current callback only supports after_iteration that takes info. Taking inspiration from XGBoost callback system it would be great if we can have before iteration that takes info, before_training, and `after

The official instructions say to use joblib for pickling PyOD models.

This fails for AutoEncoders, or any other TensorFlow-backed model as far as I can tell. The error is:

>>> dump(model, 'model.joblib')
...
TypeError: can't pickle _thread.RLock objects

Note that it's not sufficient to save the underlying Keras S

Unless I missed something, the documentation doesn't explain how to query document metadata (searching "site:montferret.dev metadata" through Google returned nothing, neither did grepping the source code).

As an example, I tried to query the og:url metadata.
I tried variations of //meta[property='og:url']::attr(content), with or without the leading //, and with or without the `attr(conte

Good first issue - pick one transformer to work on this.

A number of transformers are in fact transformers that act independently on individual series, and have some inefficient vectorization (using "nested data frames" inside). This inefficient vectorization should be removed, and the transformers moved to the transformations.series module, with deprecation.

One of two things should happe