[python-package] avoid data_has_header check in predict()

[jameslamb]

In the Python package, you can generate predictions on data stored in a delimited text file by passing a filepath to Booster.predict().

That calls LGBM_BoosterPredictForFile() in the C API, which takes a boolean argument indicating whether or not the file's first row is a header with feature names.

That argument, data_has_header, is a boolean in Booster.predict()'s interface but an int in LGBM_BoosterPredictForFile(), leading to this conversion:

LightGBM/python-package/lightgbm/basic.py

Line 964 in 7140396

int_data_has_header = 1 if data_has_header else 0

This PR proposes moving that conversion down into the if-else block corresponding to "data is a file", so it's cost is avoided on all other prediction paths.

I'm sure the cost of that one check is very very very very very small, but Booster.predict() is a latency-sensitive part of the API since it's used in model serving.

[jmoralez]

Just a small suggestion in case you want to go for it.

[github-actions]

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