data-mining

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"
            }
        ]
    }
})

We should refactor the default settings in _config.

Currently, _config has two things related to default settings:

• default "inhabitants" of key scitypes, within and outside sktime
• default parameter settings for each individual concrete estimator

I would go and locate these within the boundaries of their "natural concern":

• default parameter settings as an inspectable attrib

What's wrong?