feat: add df.bigquery.ai.forecast method to pandas dataframe accessor#2518
feat: add df.bigquery.ai.forecast method to pandas dataframe accessor#2518
df.bigquery.ai.forecast method to pandas dataframe accessor#2518Conversation
This adds a new `ai` property to `BigQueryDataFrameAccessor` which returns an `AIAccessor`. The `AIAccessor` provides a `forecast` method that calls `bigframes.bigquery.ai.forecast`, converting the Pandas DataFrame to a BigFrames DataFrame and the result back to a Pandas DataFrame. Co-authored-by: tswast <247555+tswast@users.noreply.github.com>
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| The time points column provides the time points used to generate the forecast. | ||
| The time points column must use one of the following data types: TIMESTAMP, DATE and DATETIME | ||
| model (str, default "TimesFM 2.0"): | ||
| A str value that specifies the name of the model. TimesFM 2.0 is the only supported value, and is the default value. |
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@jules According to https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast, "TimesFM 2.5" is also supported. We should link to https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast in the docstring, above.
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Added a link to the standard-sql documentation for ai.forecast and updated the model param documentation to include TimesFM 2.5.
| The DataFrame is converted to BigFrames by calling ``read_pandas``, then the forecast | ||
| is generated using ``bigframes.bigquery.ai.forecast``, and the result is | ||
| converted back to a pandas DataFrame using ``to_pandas``. |
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@jules This is an implementation detail that doesn't belong in the docstring.
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Removed the implementation details regarding read_pandas and to_pandas from the docstring.
| model: str = "TimesFM 2.0", | ||
| id_cols: Optional[Iterable[str]] = None, | ||
| horizon: int = 10, | ||
| confidence_level: float = 0.95, |
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@jules Missing the output_historical_time_series boolean parameter.
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Added the missing output_historical_time_series boolean parameter and documented it.
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| import pandas as pd | |||
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@jules missing the license header.
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Added the missing Apache 2.0 license header to tests/system/small/extensions/test_dataframe_accessor.py.
* Updated documentation to refer to BigQuery AI.FORECAST syntax. * Removed internal implementation details (`read_pandas` and `to_pandas`) from docstring. * Added missing `output_historical_time_series` parameter. * Added missing copyright headers to `tests/system/small/extensions/test_dataframe_accessor.py`. Co-authored-by: tswast <247555+tswast@users.noreply.github.com>
tswast
changed the title
df.bigquery.ai.forecast method to pandas dataframe accessor
| session = bf_session.get_global_session() | ||
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| bf_df = cast(bpd.DataFrame, session.read_pandas(self._obj)) | ||
| result = bigframes.bigquery.ai.forecast( |
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I'm curious if this triggers our logger.
My gut feeling says yes, because our logger only seems to track the call stack of annotated classes / functions. However, I still feel like to double check with you on this.
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Adds the
.bigquery.ai.forecast()method to pandas DataFrame objects, which proxies tobigframes.bigquery.ai.forecast(). Added unit tests and mocked session responses.PR created automatically by Jules for task 14604090974587392182 started by @tswast