ValueError: Found array with 0 sample(s) (shape = 0, 212) while a minimum of 1 is required by StandardScaler

[FReddig]

Hi,

I am struggling to execute a Spectral Unmixing in the EnMAP-Box. After inserting all the necessary data, I am getting the following error report:

QGIS version: 3.30.1-'s-Hertogenbosch QGIS code revision: 9035a01e Qt version: 5.15.3 Python version: 3.9.5 GDAL version: 3.6.3 GEOS version: 3.11.2-CAPI-1.17.2 PROJ version: Rel. 9.2.0, March 1st, 2023 PDAL version: 2.5.2 (git-version: 57c4e7) Algorithm started at: 2023-08-02T13:15:11 Algorithm 'Regression-based unmixing' starting… Input parameters: { 'allowWithinClassMixtures' : True, 'background' : 0, 'classProbabilities' : '', 'dataset' : 'C:/Users/Reddig/AppData/Local/Temp/processing_QGtXRX/0845ab74b16f4ed1b756776b3b5d64ff/outputClassificationDataset.pkl', 'ensembleSize' : 3, 'includeEndmember' : True, 'mixingProbabilities' : '0.4, 0.4, 0.2', 'n' : 1000, 'outputClassification' : 'Y:/CHILE23/Reddig/Spectral_library/ENDMEMBER_VALUES/FINALE/classification_layer.tif', 'outputFraction' : 'Y:/CHILE23/Reddig/Spectral_library/ENDMEMBER_VALUES/FINALE/class_fractional_layer.tif', 'outputFractionVariation' : 'Y:/CHILE23/Reddig/Spectral_library/ENDMEMBER_VALUES/FINALE/fraction_variation_layer.tif', 'raster' : 'Y:/CHILE23/Reddig/raw/enmap_export/enmap_oya_clipped.tif', 'regressor' : 'from sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LinearRegression\n\nlinearRegression = LinearRegression()\nregressor = make_pipeline(StandardScaler(), linearRegression)', 'robustFusion' : False, 'sumToOne' : False }

Create ensemble Traceback (most recent call last): File "C:\Users\Reddig\AppData\Roaming\Python\Python39\site-packages\typeguard__init__.py", line 1033, in wrapper retval = func(*args, *kwargs) File "C:\Users/Reddig/AppData/Roaming/QGIS/QGIS3\profiles\default/python/plugins\enmapboxplugin\enmapboxprocessing\algorithm\predictregressionalgorithm.py", line 96, in processAlgorithm y = dump.regressor.predict(np.transpose(X)) File "C:\Users\Reddig\AppData\Roaming\Python\Python39\site-packages\sklearn\pipeline.py", line 480, in predict Xt = transform.transform(Xt) File "C:\Users\Reddig\AppData\Roaming\Python\Python39\site-packages\sklearn\utils_set_output.py", line 140, in wrapped data_to_wrap = f(self, X, args, kwargs) File "C:\Users\Reddig\AppData\Roaming\Python\Python39\site-packages\sklearn\preprocessing_data.py", line 992, in transform X = self._validate_data( File "C:\Users\Reddig\AppData\Roaming\Python\Python39\site-packages\sklearn\base.py", line 565, in _validate_data X = check_array(X, input_name="X", check_params) File "C:\Users\Reddig\AppData\Roaming\Python\Python39\site-packages\sklearn\utils\validation.py", line 931, in check_array raise ValueError( ValueError: Found array with 0 sample(s) (shape=(0, 212)) while a minimum of 1 is required by StandardScaler.

Execution failed after 2.49 seconds

Loading resulting layers Algorithm 'Regression-based unmixing' finished

I really don't know what I am doing wrong. If you need additional information, I will deliver them instantly, since I dont know which information are needed to understand this problem.

Thank you for your help! Fabian

[janzandr]

Please share your input data (or a small subset), which can be used to reproduce the error.

[FReddig]

Yes of course, can I send you the link to a drive folder as PN?

[janzandr]

Sure andreas.janz@geo.hu-berlin.de

[janzandr]

The image that you want to unmix includes bad bands. You have to remove those first.

[FReddig]

It worked, thank you so much!