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

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: 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.

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

LET page = DOCUMENT("YOUR_URL", {
	driver: "cdp",
	ignore: {
        resources: [
            {
                url: "*",
                type: "image"
            }
        ]
    }
})

Users currently have to set the seasonal periodicity sp. We should provide an option to estimate it from the data or infer it from the date time index.

There may be some existing related functionality in pycaret and statmodels already.

• What's wrong?