adaptive.generate_targeted_samples

generate_targeted_samples(
    df,
    input_cols,
    outcome_col,
    n_new_per_fix=10,
    failed_data=None,
    distance_threshold=0.05,
)

Active Learning Engine: Generates new samples based on diagnostic failures.

It consumes the results table from sample_sufficiency. - If Input Coverage fails -> Triggers _fill_gaps (Exploration). - If Model Fit or Bootstrap fails -> Triggers _sample_uncertainty (Exploitation).

Parameters

Name	Type	Description	Default
df	pd.DataFrame	Current simulation data.	required
input_cols	List[str]	Input variable names.	required
outcome_col	str	Outcome variable name.	required
n_new_per_fix	int	Number of samples to generate per detected issue.	10
failed_data	Optional[pd.DataFrame]	Graveyard of inputs that crashed the solver.	None
distance_threshold	float	Minimum normalized distance to maintain from failed points.	0.05

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: New recommended samples.

Examples

import pandas as pd
# 1. Setup data with a massive gap in 'Length' (0-1, then 9-10)
df = pd.DataFrame({'Length': [0.1, 0.9, 9.1, 9.9], 'Signal': [1, 1, 1, 1]})

# 2. Ask for new samples to fix the gap
new_pts = generate_targeted_samples(
    df=df,
    input_cols=['Length'],
    outcome_col='Signal',
    n_new_per_fix=2
)
print(new_pts)
#    Length Refinement_Reason
# 0     5.4      Gap in Length
# 1     3.2      Gap in Length