When following the NCAA Basketball Tutorial and trying to make a prediction on a date with the model created and trained, the system throws the following error:
"IndexError: boolean index did not match indexed array along dimension 1; dimension is 96 but corresponding boolean dimension is 106"
It is thrown at this part of the code:
Traceback (most recent call last):
File "/Users/alejandrovargasperez/opt/anaconda3/bin/sflow", line 8, in
sys.exit(main())
File "/Users/alejandrovargasperez/opt/anaconda3/lib/python3.8/site-packages/alphapy/sport_flow.py", line 912, in main
model = main_pipeline(model)
File "/Users/alejandrovargasperez/opt/anaconda3/lib/python3.8/site-packages/alphapy/main.py", line 434, in main_pipeline
model = prediction_pipeline(model)
File "/Users/alejandrovargasperez/opt/anaconda3/lib/python3.8/site-packages/alphapy/main.py", line 364, in prediction_pipeline
X_all = create_interactions(model, X_all)
File "/Users/alejandrovargasperez/opt/anaconda3/lib/python3.8/site-packages/alphapy/features.py", line 1316, in create_interactions
pfeatures, pnames = get_polynomials(X[:, support], poly_degree)
To Reproduce
Steps to reproduce the behavior:
Run the command: "sflow --pdate 2016-03-01"
When finished, run SportFlow in predict mode: "sflow --predict --pdate 2016-03-15"
Description of the error
When following the NCAA Basketball Tutorial and trying to make a prediction on a date with the model created and trained, the system throws the following error:
"IndexError: boolean index did not match indexed array along dimension 1; dimension is 96 but corresponding boolean dimension is 106"
It is thrown at this part of the code:
Traceback (most recent call last): File "/Users/alejandrovargasperez/opt/anaconda3/bin/sflow", line 8, in
sys.exit(main())
File "/Users/alejandrovargasperez/opt/anaconda3/lib/python3.8/site-packages/alphapy/sport_flow.py", line 912, in main
model = main_pipeline(model)
File "/Users/alejandrovargasperez/opt/anaconda3/lib/python3.8/site-packages/alphapy/main.py", line 434, in main_pipeline
model = prediction_pipeline(model)
File "/Users/alejandrovargasperez/opt/anaconda3/lib/python3.8/site-packages/alphapy/main.py", line 364, in prediction_pipeline
X_all = create_interactions(model, X_all)
File "/Users/alejandrovargasperez/opt/anaconda3/lib/python3.8/site-packages/alphapy/features.py", line 1316, in create_interactions
pfeatures, pnames = get_polynomials(X[:, support], poly_degree)
To Reproduce Steps to reproduce the behavior: