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I'm running QSPRPred in anaconda environment with python 3.9.
I tried to run the following command sequentially and got an error at the end.
I think data_CLI and model_CLI ran OK but pred_CLI had a problem.

$ python -m qsprpred.data_CLI -i parkinsons_pivot.tsv -pr GABAAalpha -r true -sf 0.15 -fe Morgan
$ python -m qsprpred.model_CLI -pr GABAAalpha -r REG -me -s -m XGB
$ python -m qsprpred.predict_CLI -i parkinsons_pivot.tsv -pr GABAAalpha -r REG -m XGB
{
"base_dir": ".",
"debug": false,
"random_state": 1,
"input": "parkinsons_pivot.tsv",
"output": "predictions",
"ncpu": 8,
"gpus": [
"0"
],
"smilescol": "SMILES",
"properties": [
[
"GABAAalpha"
]
],
"regression": [
true
],
"model_types": [
"XGB"
],
"no_preprocessing": false,
"no_git": false
}
XGB

Traceback (most recent call last):
File "/opt/anaconda/envs/drugex/lib/python3.9/site-packages/qsprpred/predict_CLI.py", line 184, in
QSPR_predict(args)
File "/opt/anaconda/envs/drugex/lib/python3.9/site-packages/qsprpred/predict_CLI.py", line 134, in QSPR_predict
predictions = predictor.predictMols(smiles_list)
File "/opt/anaconda/envs/drugex/lib/python3.9/site-packages/qsprpred/models/interfaces.py", line 490, in predictMols
predictions = self.predict(dataset)
File "/opt/anaconda/envs/drugex/lib/python3.9/site-packages/qsprpred/models/models.py", line 302, in predict
return self.model.predict(X)
File "/opt/anaconda/envs/drugex/lib/python3.9/site-packages/xgboost/sklearn.py", line 601, in predict
return self.get_booster().predict(test_dmatrix,
File "/opt/anaconda/envs/drugex/lib/python3.9/site-packages/xgboost/sklearn.py", line 271, in get_booster
raise XGBoostError('need to call fit or load_model beforehand')
xgboost.core.XGBoostError: need to call fit or load_model beforehand

There are currently some issues with newer version of scikit-learn and the serialization code facilitated by ml2json (issue here: OlivierBeq/ml2json#5). This will hopefully be fixed soon, but stick to lower versions of scikit-learn in the meantime.

"sanitize_smiles" is called in predict_CLI.py, but this function is not defined in data/utils/smiles_standardization.py or anywhere.