compute_rf_event_weights

lstchain.reco.utils.compute_rf_event_weights(events)

Compute event-wise weights. Can be used for correcting for the different statistics present in each pointing node of the MC training sample, to avoid “jumps” in the performance of the random forests

Parameters:

events~pd.DataFrame

DL1 parameters dataframe. The table is modified in place by the

addition of a column called ‘weight’ (unless it exists already). The

column contains an event-wise weight to be used in the Random Forest

training, to give each of the telescope pointings in the training sample

the same overall weight in the training.

Returns:

pointings: ndarray of shape (number_of_pointings, 2) Alt Az (in radians)

for each of the identified telescope pointings in the input MC sample

weight_per_pointing: ndarray [number_of_pointings] weight for each of the

identified pointings. The weight is equal to the mean number of training

events per node divided by the number of training events in the specific

node. If used as sample_weight in scikit-learn, each node will have the

same total weight in the training of the Random Forests