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Description
The live pipeline version got stuck due to this runtime exception: ValueError: DataFrame.dtypes for data must be int, float, bool or category
Here is the output:
File "/app/app/preprocessors/predictions.py", line 61, in forecast
predicted_score = forecast_proper(
File "/app/app/preprocessors/predictions.py", line 30, in forecast_proper
predicted = model.predict(x)[0]
File "/usr/local/lib/python3.9/site-packages/xgboost/sklearn.py", line 1140, in predict
predts = self.get_booster().inplace_predict(
File "/usr/local/lib/python3.9/site-packages/xgboost/core.py", line 2283, in inplace_predict
data, fns, _ = _transform_pandas_df(data, enable_categorical)
File "/usr/local/lib/python3.9/site-packages/xgboost/data.py", line 378, in _transform_pandas_df
_invalid_dataframe_dtype(data)
File "/usr/local/lib/python3.9/site-packages/xgboost/data.py", line 270, in _invalid_dataframe_dtype
raise ValueError(msg)
ValueError: DataFrame.dtypes for data must be int, float, bool or category. When categorical type is supplied, The experimental DMatrix parameter`enable_categorical` must be set to `True`. Invalid columns:bfwpub: object, brainpop: object, carnegie learning: object, discoveryeducation: object, college board: object, decker & associates real life math: object, edmentum: object, educationgalaxy: object, freckle math: object, hmh ed: object, icev: object, imagine learning - edgenuity: object, istation: object, ixl math: object, khanacademy: object, renaissance: object, shmoop: object, star 360: object, stemscopes: object, legendsoflearning: object, mathsisfun: object, happynumbers: object, xtramath: object, 99math: object, studysync: object, mathplayground: object, edready: object, classhero: object, extrememath: object, mapmygrad: object, st math: object, starfall: object, mheducation: object, renaissance-go: object, stemscopes (accelerate learning): object, greatminds: object, icev online: object, prodigy: object, edgenuity: object, imagine math facts: objectMetadata
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