Since state is a reserved keyword in Truffle's guard mechanism, it should not be possible to use it in a guard definition as this can cause serious problems. The annotation processor should either reject the state keyword in guard definitions or the state temporary variable produced by the processor should be renamed to avoid this conflict.

Here's an example:
<img width="629" alt="screen

Currently you have to click on the devtools icon and then toggle errors button.

This project's documentation includes a list of winning solutions for machine learning competitions which used LightGBM.

https://github.com/microsoft/LightGBM/tree/master/examples#machine-learning-challenge-winning-solutions

With one exception (), this has not been updated since December 2019.

How to close this issue

Look through "completed" competitions (from December 2019 to now)

I am trying to create a CV using leave-one-out i.e predicting the next rolling day in my test-data.

I have a train data X_train and test data X_test, and fitting a model using

model.fit(pd.concat((X_train,X_test)))

from fbprophet.diagnostics import cross_validation
cutoffs = X_test["ds"].values
df_cv = cross_validation(model,cutoffs=cutoffs,horizon = '1 day')

th

Problem:
catboost version: 0.23.2
Operating System: all
Tutorial: https://github.com/catboost/tutorials/blob/master/custom_loss/custom_metric_tutorial.md

Impossible to use custom metric (С++).

Code example

from catboost import CatBoost
train_data = [[1, 4, 5, 6],
 

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.