When running the Sirsam Na randomforest test data (under tests/test_data/siram - currently part of bren-testing-and-restructure branch) there is an error when running a prediction.
There are 9 covariate datasets in total. All have one band except for regolith_ternary.tif which has 3 (RGB). When the prediction is run on the model, scikit-learn complains that the model contains 11 features but only 9 have been provided. learn runs fine.
This error goes away if the model is retrained without regolith_ternary.tif, so it appears to be some sort of issue where multiband data is not being read correctly for prediction.
When running the Sirsam Na randomforest test data (under tests/test_data/siram - currently part of bren-testing-and-restructure branch) there is an error when running a prediction.
There are 9 covariate datasets in total. All have one band except for
regolith_ternary.tif
which has 3 (RGB). When the prediction is run on the model, scikit-learn complains that the model contains 11 features but only 9 have been provided.learn
runs fine.This error goes away if the model is retrained without
regolith_ternary.tif
, so it appears to be some sort of issue where multiband data is not being read correctly for prediction.