arumds

I am in a similar situation to stop async process. The promise from the future() is piped to the file_rows() reactive output value. Based on the tip by @raphaelvannson i...

``` observeEvent(input$rec_cancel, { async_pid

`Sys.getpid()` correclty fetches the PID value and therefore the kill command works by killing the corresponding PID. I tired to print PIDs to log file to see if they return...

Hi Brian, You are correct that my input file has 2500 samples and the issue could be with the sample size. Here is the big data i am trying to...

I figured this out. I have the same problem also in Ubuntu machine. Adding -lcurl -llzma -lbz2 to the last of the code did the trick. Change the last line,...

@jmoralez tried setting to `grad = np.where(residual >= 0, 0.5, -0.5)` ``` params={ "objective": median_loss, }, cv_result = lgb.cv(params, dtrain, nfold=n_folds, metrics='rmse', stratified=False, return_cvbooster=True) ``` Log: ``` [LightGBM] [Warning] Using...

@jmoralez Attached is a test dtrain binary file which can be used to reproduce as below: ``` dataset_from_file = lgb.Dataset(data="test.bin") params={"objective": median_loss, 'num_leaves': 32, 'verbosity': -1, 'metrics': 'l2'} cv_hist =...

You mean to get the model from lgb.train after lgb.cv and inspect the trees? If so, yes there seem to be only root. The hyper_params from the lgb.cv() and BayesianOptimization...

@jmoralez The hyper parameter boundaries for tuning are shown below: ``` hyperparam_boundaries = {'num_leaves': (100, 300), 'max_depth': (10, 20), 'feature_fraction': (0.7, 1), 'bagging_fraction': (0.7, 1), 'min_data_in_leaf': (10, 25), 'min_gain_to_split': (0.01,0.05),...

@jmoralez is there anything i am missing out here?