feat: add DataFrame.ai.forecast() support #1828
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bigframes/operations/ai.py
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| self._df: bigframes.dataframe.DataFrame = df | ||
| self._base_bqml = ml_core.BaseBqml(session=df._session) |
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Can you inject BaseBqml as a constructor parameter (default to None)? It might make testing easier if we want to fake/mock BaseBqml in the future.
| "confidence_level": confidence_level, | ||
| } | ||
| if id_columns: | ||
| options["id_cols"] = id_columns |
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Maybe we should verify the validity of timestamp_column and data_column, and raise error if necessary?
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You mean the data types? We rely on the checks from the backend. Basically client libraries shouldn't do too much checks unless those are only specific to client.
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No I meant the existence of these columns. If the user has made some typos, we want the code to fail fast. The other methods have this check too.
bigframes/operations/ai.py
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| self._df: bigframes.dataframe.DataFrame = df | ||
| self._base_bqml: ml_core.BaseBqml = base_bqml |
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Let's also provide a default BaseBqml instance if None is provided: It should be just ml_core.BaseBqml(df._session).
By doing this, we don't need to provide one in that dataframe ai property.
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Thanks! |
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Fixes #<issue_number_goes_here> 🦕