diagnostics.validate_simulation

validate_simulation(df, input_cols, outcome_col)

Validates simulation data, coercing to numeric and removing invalid rows.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	The raw dataframe containing input columns and the outcome column.	required
input_cols	List[str]	List of input variable names.	required
outcome_col	str	Name of the outcome variable.	required

Returns

Name	Type	Description
	Tuple[pd.DataFrame, pd.DataFrame]	* `df_clean`: The validated, numeric dataframe ready for analysis. * `df_removed`: A dataframe containing the rows that were dropped.

Raises

Name	Type	Description
	ValidationError	If columns are missing, types are wrong, or too few valid rows remain.

Examples

import numpy as np
import pandas as pd
# 12 rows, one of which has a non-numeric input
length = list(np.linspace(1.0, 5.0, 12))
length[3] = 'BadValue'
df = pd.DataFrame({'Length': length, 'Signal': np.linspace(0.5, 2.0, 12)})

# Validate
clean, removed = validate_simulation(df, ['Length'], 'Signal')
print(f"Clean rows: {len(clean)}")
print(f"Removed rows: {len(removed)}")