Open bluetyson opened 3 years ago
Basic config when encountered the problem :-
mpiexec -n 3 uncoverml learn N032Dummy.yaml
[same error if a percentage OOS or a separate shapefile]
learning:
algorithm: randomforest
arguments:
n_estimators: 50
target_transform: standardise
random_state: 42
validation:
out_of_sample:
percentage: 0.2
shapefile: smalloutofsample.shp
property: target
k-fold:
folds: 3
parallel: True
random_seed: 42
features:
- type: ordinal
files:
- directory: F:\Modelling
transforms:
- centre
- standardise
imputation: mean
targets:
file: F:/targetsmain.shp
property: target
mask:
file: F:/mask.tif
retain: 1
prediction:
quantiles: 0.95
outbands: 4
output:
#plot_feature_ranks: True
#plot_intersection: True
#plot_real_vs_pred: True
#plot_correlation: True
#plot_target_scaling: True
directory: F:/RESULTS/
The problem occurs when doing OOS validation and multiprocessing by the looks. A .model file is created and looks fine when I check it... as in, I loaded one created and it had right number of feature importances, not tried prediction. Same process/data worked on out of sample when not run multiprocessing. Model exists when checked in predict.py - kwargs and RandomForestTransformed get returned ok - and that branch clearly works fine in general - I have run quite a lot of multiprocessing regressions with no OOS section with no problem.