I am using PyCaret and get an error.
AttributeError: 'SimpleImputer' object has no attribute '_validate_data'
Trying to create a basic instance.
# Create a basic PyCaret instance
import pycaret
from pycaret.regression import *
mlb_pycaret = setup(data = pycaret_df, target = 'pts', train_size = 0.8, numeric_features = ['home',
'first_time_pitcher'], session_id = 123)
All my variables are numeric (I coerced two of them, which are boolean). My target variable is label
and this is by default.
I also installed PyCaret
, imported its regression, and re-installed scikit learn
, imported SimpleImputer
as from sklearn.impute import SimpleImputer
OBP_avg Numeric
SLG_avg Numeric
SB_avg Numeric
RBI_avg Numeric
R_avg Numeric
home Numeric
first_time_pitcher Numeric
park_ratio_OBP Numeric
park_ratio_SLG Numeric
SO_avg_p Numeric
pts_500_parkadj_p Numeric
pts_500_parkadj Numeric
SLG_avg_parkadj Numeric
OPS_avg_parkadj Numeric
SLG_avg_parkadj_p Numeric
OPS_avg_parkadj_p Numeric
pts_BxP Numeric
SLG_BxP Numeric
OPS_BxP Numeric
whip_SO_BxP Numeric
whip_SO_B Numeric
whip_SO_B_parkadj Numeric
order Numeric
ops x pts_500 order15 Numeric
ops x pts_500 parkadj Numeric
ops23 x pts_500 Numeric
ops x pts_500 orderadj Numeric
whip_p Numeric
whip_SO_p Numeric
whip_SO_parkadj_p Numeric
whip_parkadj_p Numeric
pts Label
My traceback is the following:
The problem here is with the imputation. The default per pycaret documentation is 'simple' but in this case, you need to make that imputation_type='iterative'
for it to work.
It's incompatibility of library, install pycaret again with: pip install pycaret pandas shap
Good day all. What helped me is installing pycaret=='2.3.10 ' and scikit-learn='0.23.2' at the same time. These two version are compatible and all works fine. I installed scikit-learn using conda as the older versions are not available through pip, and I installed Pycaret using pip3. I hope this helps all who have struggled to get this working like I did.
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