monad.ui.target_function.has_incomplete_training_window

monad.ui.target_function.has_incomplete_training_window(ctx, required_days)

Checks if the training window is too short for target computation.

from monad.ui.target_function import has_incomplete_training_window

def target_fn(history: Events, future: Events, attribites: Attributes, ctx: Dict) -> np.ndarray:
    # trim the future to the desired target window
    target_window_days = 21
    if has_incomplete_training_window(ctx, target_window_days):
      return None
	...

ctx : Dict
Context dictionary containing mode and timestamp information. Typically, you should pass the ctx object from the target function’s input.

required_days : int
Number of days required for prediction window.

bool, true if training window is shorter than required days, false otherwise. Always returns False for non-training modes.