Enhance DataCollector to validate model_reporters functions

[peter-kinger]

Summary

This PR enhances the DataCollector class by adding a comprehensive validation system for model reporters. The main goal is to provide clearer feedback when reporters are misconfigured, while maintaining backward compatibility.

Motive

The original DataCollector implementation had several limitations:

1. No validation for different types of model reporters
2. Silent failures or unclear error messages when reporters were misconfigured
3. Inconsistent handling of method calls and function parameters
4. No early warning system for potential issues

These issues made debugging difficult and led to confusion when setting up model reporters.

Implementation

1. New Validation Method:

def _validate_model_reporter(self, name, reporter, model):
    """Validates four types of model reporters:
    1. Lambda functions
    2. Method references (as strings)
    3. Attribute strings
    4. Function lists with parameters
    """

2. Modified Collection Logic:

def collect(self, model):
    if self.model_reporters:
        for var, reporter in self.model_reporters.items():
            # Add validation before collection
            self._validate_model_reporter(var, reporter, model)
            # Existing collection logic continues...

3. Warning System:

• Replaced hard errors with warning messages
• Added specific warnings for each reporter type
• Included examples in warning messages

Usage Examples

1. Lambda Functions:

# Valid usage
model_reporters = {
    "Agent Count": lambda m: len(m.agents)
}

# Invalid usage (will show warning)
model_reporters = {
    "Bad Lambda": lambda m: m.nonexistent_attr
}

2. Method References:

# Valid usage
model_reporters = {
    "Grid Size": "get_grid_size"  # As string
}

# Invalid usage (will show warning)
model_reporters = {
    "Grid Size": self.get_grid_size  # Direct reference
}

3. Attribute Strings:

# Valid usage
model_reporters = {
    "Total Wealth": "total_wealth"
}

# Invalid usage (will show warning)
model_reporters = {
    "Status": "nonexistent_attribute"
}

4. Function Lists:

# Valid usage
model_reporters = {
    "Custom": [calculate_metric, [model, param]]
}

# Invalid usage (will show warning)
model_reporters = {
    "Bad Function": ["not_callable", [1, 2]]
}

Additional Notes

1. Test Coverage:

• Added comprehensive test suite in test_model_reporters.py
• Tests cover both valid and invalid configurations
• Includes edge cases and error conditions

2. Backward Compatibility:

• All existing code continues to work
• Warnings can be suppressed if desired
• No breaking changes to the API

3. Documentation:

• Updated docstrings with clear examples
• Added warning messages with helpful suggestions
• Included migration guide for existing code

4. Files Modified:

• datacollection.py: the only one py code changed in mesa offical repository.

5. Dependencies:

• No new dependencies added
• Uses standard Python warnings module

datacollection_new.zip

[EwoutH]

Thanks for the PR, sounds interesting.

What's the performance overhead?

[quaquel]

why issue a warning instead of an exception?

[quaquel]

Does this imply that the validation is done every single time you try to collect the data? That seems inefficient and overkill.