Data Type Validation: Checks that both scores and metric_weights are dictionaries. This is essential because the function operates on key-value pairs where metric names are keys, ensuring that operations such as accessing or modifying data based on these keys can be performed correctly.
if not isinstance(scores, dict) or not isinstance(metric_weights, dict):
raise TypeError("calculate_composite_score(): Both 'scores' and 'metric_weights' must be dictionaries.")
Value Validations
Non-Empty Dictionaries: Validates that neither scores nor metric_weights is empty. This is crucial because processing an empty dictionary would not only be meaningless but could also lead to division by zero errors during the composite score calculation.
if not scores or not metric_weights:
raise ValueError("calculate_composite_score(): 'scores' and 'metric_weights' dictionaries cannot be empty.")
Consistency Between Dictionaries: Ensures that every metric listed in scores has a corresponding weight in metric_weights. Missing weights could lead to incorrect calculations or unintentional exclusion of important metrics from the composite score calculation.
missing_metrics = set(scores.keys()) - set(metric_weights.keys())
if missing_metrics:
raise ValueError(f"calculate_composite_score(): Missing metric weights for: {', '.join(missing_metrics)}. Ensure 'metric_weights' includes all necessary metrics.")
Calculation Methodology
The function calculates the composite score by weighting each individual metric score by its corresponding weight and then normalizing this sum by the total weight. This methodology ensures a balanced evaluation across different metrics based on their assigned importance.
Exception Handling
An additional try-except block is used to catch any unexpected errors during the score calculation, such as possible data type mismatches within arithmetic operations or other anomalies not caught by the initial type and value checks.
try:
composite_score = sum(score * metric_weights.get(metric, 0) for metric, score in scores.items()) / sum(metric_weights.values())
except Exception as e:
raise ValueError(f"calculate_composite_score(): Error calculating composite score: {str(e)}")
This approach ensures robustness and provides clear, actionable error messages that can help users quickly identify and fix issues in their input data or handling logic. By meticulously validating input types and values and by providing detailed feedback in error messages, the function aids users in maintaining the integrity and reliability of their model evaluation processes.
Error Handling in
calculate_composite_score()Type Validations
scoresandmetric_weightsare dictionaries. This is essential because the function operates on key-value pairs where metric names are keys, ensuring that operations such as accessing or modifying data based on these keys can be performed correctly.Value Validations
scoresnormetric_weightsis empty. This is crucial because processing an empty dictionary would not only be meaningless but could also lead to division by zero errors during the composite score calculation.scoreshas a corresponding weight inmetric_weights. Missing weights could lead to incorrect calculations or unintentional exclusion of important metrics from the composite score calculation.Calculation Methodology
The function calculates the composite score by weighting each individual metric score by its corresponding weight and then normalizing this sum by the total weight. This methodology ensures a balanced evaluation across different metrics based on their assigned importance.
Exception Handling
An additional try-except block is used to catch any unexpected errors during the score calculation, such as possible data type mismatches within arithmetic operations or other anomalies not caught by the initial type and value checks.
This approach ensures robustness and provides clear, actionable error messages that can help users quickly identify and fix issues in their input data or handling logic. By meticulously validating input types and values and by providing detailed feedback in error messages, the function aids users in maintaining the integrity and reliability of their model evaluation processes.