Currently register looks like:

   private static <T> void register(Map<T, T> substitutions, T annotated, T original, T target) {
        if (annotated != null) {
            guarantee(!substitutions.containsKey(annotated) || substitutions.get(annotated) == original || substitutions.get(annotated) == target, "Already registered: %s", annotated);
            substitutions.put(annotated,

In recent versions (can't say from exactly when), there seems to be an off-by-one error in dcc.DatePickerRange. I set max_date_allowed = datetime.today().date(), but in the calendar, yesterday is the maximum date allowed. I see it in my apps, and it is also present in the first example on the DatePickerRange documentation page.

E

from fbprophet.serialize import model_to_json, model_from_json
with open("serialized_model.json", "r") as fin:
    fb_model = model_from_json(json.load(fin))

df_cv = cross_validation(fb_model, ............)

It produces this error,

File "C:\Users\XXXXX\.conda\envs\fbprophet7\lib\site-packages\fbprophet\diagnostics.py", line 295, in prophet_copy
    stan_backend=m.st

Summary

scikit-learn supports a test that people writing scikit-learn extensions can use to check API compatibility with the rest of the scikit-learn ecosystem. Such a test should be added for the code in python-package/lightgbm/dask.py.

Motivation

The discussion in #3883 focused a lot on compatibility of the dask estimators, like DaskLGBMClassifier, with the broader scikit-le

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.

I'm happy to announce that I'll be writing the second edition of Data Science at the Command Line (O'Reilly, 2014). This issue explains why I think a second edition is needed, lists what changes I plan to make, and presents a tentative outline. Finally, I have a few words about the process and giving feedback.

Why a second edition?

While the command line as a technology and as a way of w

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.