Defaults¶
In this section, we explain which parameters are set by MLAir during the ExperimentSetup
if not specified by the
user. This is important information for example if a new Custom Data Handler is implemented.
parameter |
default |
comment |
---|---|---|
batch_path |
||
batch_size |
|
|
bootstrap_path |
||
competitor_path |
||
competitors |
|
|
create_new_bootstraps |
|
|
create_new_model |
|
|
data_handler |
|
|
data_origin |
||
data_path |
||
debug |
|
MLAir checks if it is running in debug mode and stores this dimensions |
end |
|
|
epochs |
|
This is just a placeholder to prevent unintended longish training |
evaluate_bootstraps |
|
Bootstrapping may take some time. |
experiment_name |
||
experiment_path |
||
extreme_values |
|
|
extremes_on_right_tail_only |
|
Could be used for skewed distributions |
forecast_path |
||
fraction_of_training |
||
hostname |
||
hpc_hosts |
||
interpolation_limit |
|
|
interpolation_method |
|
|
logging_path |
||
login_nodes |
||
model_class |
||
model_path |
||
neighbors |
||
number_of_bootstraps |
||
overwrite_local_data |
||
permute_data |
|
|
plot_list |
||
plot_path |
||
start |
|
|
stations |
||
statistics_per_var |
||
target_dim |
||
target_var |
||
test_start |
||
test_end |
||
test_min_length |
||
time_dim |
||
train_model |
||
train_end |
||
train_min_length |
||
train_start |
||
transformation |
|
implement all further transformation functionality inside your custom data handler |
use_all_stations_on_all_data_sets |
||
use_multiprocessing |
|
is used if MLAir is not running in debug mode |
use_multiprocessing_on_debug |
|
is used if MLAir is running in debug mode |
upsampling |
||
val_end |
||
val_min_length |
||
val_start |
||
variables |
||
window_history_size |
|
|
window_lead_time |
|