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

LightGBM supports aliases for most parameter. For example, you can use bagging_fraction or sub_row to say "% of rows sampled during bagging".

The new dask API added in #3515 should be changed to support such aliases for the parameters it references.

See these comments for details on a few parameters that need to be updated to use aliases.

• https://github.com/microsoft/L

Hi!

There is no opportunity to get fitted models after cv, because catboost.cv returns only evaluation metric scores. At the same time popular ml libraries have such option in some kind.

For LightGBM there is optional argument return_cvbooster:

cv = lgb.cv(params, X_train, show_stdv=False, stratified=True

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.