Fix: Schelling Model Neighbor Similarity Calculation #2518
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Performance benchmarks:
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| else: | ||
| self.model.happy += 1 | ||
| # If unhappy, move to a random empty cell | ||
| if similarity_fraction < self.model.homophily / 8.0: |
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why not change homopily at the model level to be a fraction? That makes the model independent of the neighborhood size.
| if hasattr(neighbor, "type") and neighbor.type == self.type | ||
| ] | ||
| total_neighbors = [ | ||
| neighbor for neighbor in neighbors if hasattr(neighbor, "type") |
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you iterate twice, with one check being done in both. You can make this more efficient by looping only once.
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@Sahil-Chhoker Thanks for your PR. When do you expect to be able to incorporate @quaquel's feedback? If you have any question about it feel free to ask! |
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Thank you, @quaquel, for your review! Could you please provide more details about the model and how can I can make changes to it? |
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My feedback is quite clear, and there is a link to a more detailed description of the model in the original issue. I requested 2 well-defined changes to this PR: change From your reaction, I therefore deduce that you are not particularly familiar with agent-based modeling. |
| similar_neighbors = 0 | ||
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| for neighbor in neighbors: | ||
| if hasattr(neighbor, "type"): # Exclude empty cells |
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this is not needed, iter_neighbors returns a list of agents. In this model all agents have the type attribute.
| self.pos, moore=True, radius=self.model.radius | ||
| ) | ||
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| # Filter out empty cells | ||
| similar_neighbors = [ | ||
| neighbor | ||
| for neighbor in neighbors | ||
| if hasattr(neighbor, "type") and neighbor.type == self.type | ||
| ] | ||
| total_neighbors = [ | ||
| neighbor for neighbor in neighbors if hasattr(neighbor, "type") | ||
| ] | ||
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| # Calculate fraction of similar neighbors | ||
| if len(total_neighbors) > 0: | ||
| similarity_fraction = len(similar_neighbors) / len(total_neighbors) | ||
| valid_neighbors = 0 | ||
| similar_neighbors = 0 | ||
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| for neighbor in neighbors: | ||
| if hasattr(neighbor, "type"): # Exclude empty cells | ||
| valid_neighbors += 1 | ||
| if neighbor.type == self.type: # Count similar neighbors | ||
| similar_neighbors += 1 | ||
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| # Calculate the fraction of similar neighbors | ||
| if valid_neighbors > 0: | ||
| similarity_fraction = similar_neighbors / valid_neighbors | ||
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| # If unhappy, move to a random empty cell | ||
| if similarity_fraction < self.model.homophily / 8.0: | ||
| if similarity_fraction < self.model.homophily: | ||
| self.model.grid.move_to_empty(self) | ||
| else: | ||
| self.model.happy += 1 |
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neighbors = self.model.grid.get_neighbors(
self.pos, moore=True, radius=self.model.radius
)
# Count similar neighbors
similar_neighbors = len([n for n in neighbors if n.type == self.type])
# Calculate the fraction of similar neighbors
if (valid_neighbors := len(neighbors) )> 0:
similarity_fraction = similar_neighbors / valid_neighbors
# If unhappy, move to a random empty cell
if similarity_fraction < self.model.homophily:
self.model.grid.move_to_empty(self)
else:
self.model.happy += 1
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Please switch from currently, as you can see the tests fail because the model won't run. The list expression exhausts the iterator, so getting the number of valid neighbors fails. |
EwoutH
added
trigger-benchmarks
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Performance benchmarks:
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| self.pos, moore=True, radius=self.model.radius | ||
| ) | ||
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| # Count similar neighbors | ||
| similar = sum(neighbor.type == self.type for neighbor in neighbors) | ||
| similar_neighbors = len([n for n in neighbors if n.type == self.type]) |
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The model looks significantly slower. I’m curious if we could minimize that performance degradation, I suspect the main problem is with this line doing additional work.
(But not sure, haven’t profiled it)
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The slowdown is not due to this line of code but because the behavior changes a lot. Prior to this PR, the model would reach steady state in a few ticks. Now, it can take many ticks and frequently the model never reaches stable state.
Summary
Fixed #2515 Schelling segregation model to calculate agent happiness using neighbor similarity fraction, aligning with Wikipedia and NetLogo standards.
Bug / Issue
Current model incorrectly counts empty spaces as neighbors, leading to inaccurate agent happiness determination.
Implementation
Modified
SchellingAgent.step()to: