TabpfnHyperparams

tabpfn_model.TabpfnHyperparams · inherits PydanticBaseModel

(No description provided)

TabpfnModel

tabpfn_model.TabpfnModel · inherits BaseModel

(No description provided)

__init__(self, params: Dict[str, Any], settings: Dict[str, Any])

Parameter	Type	Default	Description
params	Dict[str, Any]	-	(undocumented)
settings	Dict[str, Any]	-	(undocumented)

train(self, y_context: np.ndarray, y_target: np.ndarray, timestamps_context: np.ndarray, timestamps_target: np.ndarray, x_context: Optional[np.ndarray] = None, x_target: Optional[np.ndarray] = None, **kwargs) -> "TabpfnModel"

(No summary provided)

Parameters:

Parameter	Type	Default	Description
y_context	np.ndarray	-	(undocumented)
y_target	np.ndarray	-	(undocumented)
timestamps_context	np.ndarray	-	(undocumented)
timestamps_target	np.ndarray	-	(undocumented)
x_context	Optional[np.ndarray]	None	(undocumented)
x_target	Optional[np.ndarray]	None	(undocumented)
**kwargs		-	(undocumented)

Returns: TabpfnModel (the fitted model; sets self.is_fitted).

predict(self, y_context: np.ndarray, timestamps_context: np.ndarray, timestamps_target: np.ndarray, x_context: Optional[np.ndarray] = None, x_target: Optional[np.ndarray] = None, **kwargs: dict)

(No summary provided)

Parameters:

Parameter	Type	Default	Description
y_context	np.ndarray	-	(undocumented)
timestamps_context	np.ndarray	-	(undocumented)
timestamps_target	np.ndarray	-	(undocumented)
x_context	Optional[np.ndarray]	None	(undocumented)
x_target	Optional[np.ndarray]	None	(undocumented)
**kwargs	dict	-	(undocumented)

Returns: np.ndarray (The prediction array).

make_time_features

tabpfn_model.make_time_features

Produce basic cyclic time features for positions 0..n-1. Mirrors TabPFN-TS style feature engineering for univariate forecasting.

Parameter	Type	Default	Description
n	int	-	(undocumented)

Returns: pd.DataFrame.