[Model] Distributed and Non-distributed version of the models giving different result about mlforecast HOT 8 OPEN

Hey. I'm not sure if there's a guarantee that the local and distributed models will be the same. Once you have both you can compare them with the trees_to_dataframe method, e.g.

dstr_df = fcst_dstr.models_['SparkXGBForecast'].get_booster().trees_to_dataframe()
local_df = fcst.models_['XGBregressor'].get_booster().trees_to_dataframe()

About the predict method, how are you running it?

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Sure! Thanks I will try checking the trees!

Another thing I noticed was that for SparkXGBForecast, it was giving different result with the same dataset, whereas non distributed version it was consistent results.
I didn't set any random seed to neither of the models ..

from xgboost.spark import SparkXGBRegressor
spark_xgb = SparkXGBRegressor(num_workers=8, label_col='target', features_col='features')
xgb_regressor_model = spark_xgb.fit(train_sf_tf)
transformed_test_spark_dataframe = spark_xgb.predict(test_spark_dataframe)

With the last line, I am getting this error.
AttributeError: 'SparkXGBRegressor' object has no attribute 'predict'

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I think you should use the trained model, i.e. xgb_regressor_model.predict instead of spark_xgb.predict

from mlforecast.

Still getting the same error when I do ..
transformed_test_spark_dataframe = xgb_regressor_model.predict(test_spark_dataframe)
AttributeError: 'SparkXGBRegressorModel' object has no attribute 'predict'

from mlforecast.

Hmm, if it's an MLlib estimator it may be called transform instead of predict. Can you try that? If it works I think you should open an issue in XGBoost so that they update the documentation.

from mlforecast.

Hmm, if it's an MLlib estimator it may be called transform instead of predict. Can you try that? If it works I think you should open an issue in XGBoost so that they update the documentation.

Yes, the spark interface uses the name transform instead of predict to align with sparkml. Did you find the document being inconsistent in XGBoost?

Hey. I'm not sure if there's a guarantee that the local and distributed models will be the same

No, the training results are expected to differ. However, given a trained model, predictions are expected to be the same.

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