Migrate all Python code from old-fashioned format() functions, formatting % operators and simple concatenations (+) to modern f-strings (brief guide). They are known to be the fastest approach and also increase code readability.

![image](https://user-images.githubusercontent.com/25141164/112898582-a

Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.

Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)

Problem: the approximate method can still be slow for many trees
catboost version: master
Operating System: ubuntu 18.04
CPU: i9
GPU: RTX2080

Would be good to be able to specify how many trees to use for shapley. The model.predict and prediction_type versions allow this. lgbm/xgb allow this.

It's been awhile since I updated e2e tests and there are some of them that are filing (most of them are related to examples).

Also, we need to add e2e tests that cover headers and cookies for both drivers.

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

Is your feature request related to a problem? Please describe.
Currently, window length parameters (e.g. window_length or initial_window in SlidingWindowSplitter) have to be integers and denote the absolute number of time points, similar to how scikit-learn handles fractions in their cross-validation functionality.

Describe the solution you'd like
Add support for floats denotin

• What's your use case?