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

One unit test in the R package is currently broken. Steps to reproduce on Mac

export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8
Rscript build_r.R
cd R-package/tests
Rscript testthat.R

This results in the following error at the ends of the logs

[LightGBM] [Info] Saving data to binary file /var/folders/xq/wktq4zdx4jd3qdpk34d28m940000gn/T//RtmpiY1DzV/lgb.Dataset_1555

I have been recreating the plot_component daily and weekly graphs using the forecast dataframe.

forecast = m.predict(future)
fig = m.plot_components(forecast)

Recreating the daily component is straight forward, as it is repeated for every day in the dataset. So can be plotted as:

forecast['daily][:n].plot() 
# Where n = number of readings in 24hr period

However, t

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.