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

Problem:

_catboost.pyx in _catboost._set_features_order_data_pd_data_frame()

_catboost.pyx in _catboost.get_cat_factor_bytes_representation()

CatBoostError: Invalid type for cat_feature[non-default value idx=1,feature_idx=336]=2.0 : cat_features must be integer or string, real number values and NaN values should be converted to string.

Could you also print a feature name, not o

For the autoencoder in pyod, how do I adjust the learning rate?

Currently, there is a lot of repetitive boilerplate in the datasets modules, and no tests for individual loaders such as load_PBS_dataset, load_japanese_vowels, etc

What should be done here:

• reduce boilerplate, refactor common code into single location
• ensure function signatures are the same, deprecate if needed
• add an argument return_mtype such as in the synthetic data gener

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