MultiModalPredictor.predict¶
- MultiModalPredictor.predict(data: DataFrame | dict | list | str, candidate_data: DataFrame | dict | list | None = None, id_mappings: Dict[str, Dict] | Dict[str, Series] | None = None, as_pandas: bool | None = None, realtime: bool | None = False, save_results: bool | None = None, **kwargs)[source]¶
预测新数据的标签列值。
- Parameters:
data – 用于进行预测的数据。应包含与训练数据相同的列名，并遵循相同的格式（除了label列）。
candidate_data – The candidate data from which to search the query data’s matches.
id_mappings – Id-to-content mappings. The contents can be text, image, etc. This is used when data contain the query/response identifiers instead of their contents.
as_pandas – Whether to return the output as a pandas DataFrame(Series) (True) or numpy array (False).
realtime – Whether to do realtime inference, which is efficient for small data (default False). If provided None, we would infer it on based on the data modalities and sample number.
save_results – 是否保存预测结果（目前仅适用于检测）
**kwargs – 传递给底层学习器的预测方法的额外关键字参数。 例如，as_coco 用于目标检测任务。
- Returns:
预测数组，每个数组对应给定数据集中的每一行。
格式取决于特定的学习器和提供的参数。