feat: Add CI integration for balance validation (Issue #68)#69
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Co-authored-by: SorraTheOrc <250240+SorraTheOrc@users.noreply.github.com>
Co-authored-by: SorraTheOrc <250240+SorraTheOrc@users.noreply.github.com>
Co-authored-by: SorraTheOrc <250240+SorraTheOrc@users.noreply.github.com>
Co-authored-by: SorraTheOrc <250240+SorraTheOrc@users.noreply.github.com>
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@copilot When running the baseline-validation workflow in this PR the following error occur: Run python scripts/aggregate_sweep_results.py ingest build/ci_sweeps |
Co-authored-by: SorraTheOrc <250240+SorraTheOrc@users.noreply.github.com>
Fixed in 507d0f5. The to: |
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Pull request overview
This PR implements CI integration for balance validation to detect game balance regressions through automated testing. The implementation adds a GitHub Actions workflow that runs nightly parameter sweeps, compares results against a baseline, and alerts on significant regressions in strategy stability or win rates.
Key changes include:
- GitHub Actions workflow with scheduled runs, manual triggers, and automatic baseline updates
- Python script for baseline management with compare, create, update, and visualization commands
- Comprehensive test suite with 32 tests covering baseline management and regression detection
- CI-optimized sweep configuration with reduced parameter grid for fast validation
Reviewed changes
Copilot reviewed 5 out of 5 changed files in this pull request and generated 6 comments.
Show a summary per file
| File | Description |
|---|---|
.github/workflows/balance-validation.yml |
GitHub Actions workflow for automated balance validation with baseline comparison and PR comments |
scripts/manage_balance_baseline.py |
Baseline management script with CLI commands for comparing, updating, and visualizing balance data |
tests/scripts/test_balance_validation.py |
Comprehensive test suite covering baseline creation, regression detection, and threshold configuration |
content/config/ci_balance_sweeps.yml |
CI-optimized sweep configuration with reduced parameter grid (45 combinations) |
content/baselines/balance_baseline.json |
Initial placeholder baseline data to be replaced by first CI run |
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| # Get threshold config from baseline or use defaults | ||
| thresholds = baseline.get("thresholds", {}) | ||
| config = BaselineConfig.from_dict(thresholds) | ||
| # Override with CLI threshold if provided | ||
| config.stability_delta_warning = stability_threshold | ||
| config.stability_delta_failure = stability_threshold * 2 |
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The comparison logic in compare_against_baseline overrides the threshold configuration from the baseline file (lines 409-411). This means that custom thresholds stored in the baseline's thresholds section will be ignored in favor of the CLI parameter. This could lead to unexpected behavior where a baseline has specific thresholds configured, but they get overridden every time.
Consider either:
- Only override if the CLI parameter differs from the default value
- Document this behavior clearly in the function docstring
- Add a warning when overriding baseline thresholds
| def compute_win_rate(strategy_stats: dict[str, Any]) -> float: | ||
| """Compute win rate from strategy statistics. | ||
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| Win is defined as stability >= 0.5. | ||
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| Parameters | ||
| ---------- | ||
| strategy_stats | ||
| Statistics for a single strategy. | ||
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| Returns | ||
| ------- | ||
| float | ||
| Win rate as a fraction (0.0 to 1.0). | ||
| """ | ||
| avg_stability = strategy_stats.get("avg_stability", 0.0) | ||
| # Approximate win rate based on average stability | ||
| # In actual sweep data, we'd count individual games | ||
| if avg_stability >= 0.5: | ||
| # Scale win rate based on how far above threshold | ||
| return min(1.0, 0.5 + (avg_stability - 0.5)) | ||
| else: | ||
| # Scale win rate based on how far below threshold | ||
| return max(0.0, avg_stability) | ||
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The win rate calculation logic in compute_win_rate uses a simple approximation based on average stability, but this doesn't accurately reflect the actual win rate definition stated in the docstring ("Win is defined as stability >= 0.5"). The current implementation:
- For stability >= 0.5: returns
0.5 + (avg_stability - 0.5), which gives 0.5 for stability=0.5, 1.0 for stability=1.0 - For stability < 0.5: returns
avg_stability, which gives values between 0 and 0.5
This approximation assumes a linear relationship between average stability and win rate, but if a strategy has high variance (some runs well above 0.5, some well below), the approximation will be inaccurate. Consider either:
- Documenting this as an approximation used when individual run data isn't available
- Storing actual win counts in the sweep summary instead of approximating
- Computing win rate from min/max stability bounds if available
| sweep = load_sweep_summary(sweep_path) | ||
| if not sweep: | ||
| raise ValueError(f"Could not load sweep data from {sweep_path}") |
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The error message "Could not load sweep data from {sweep_path}" could be more helpful by including the underlying reason for the failure. Currently, load_sweep_summary returns None if the file doesn't exist, but there could be other reasons (permission denied, invalid JSON, etc.). Consider catching the specific exception and including it in the error message for better debugging.
| sweep = load_sweep_summary(sweep_path) | |
| if not sweep: | |
| raise ValueError(f"Could not load sweep data from {sweep_path}") | |
| try: | |
| sweep = load_sweep_summary(sweep_path) | |
| except Exception as e: | |
| raise ValueError(f"Could not load sweep data from {sweep_path}: {type(e).__name__}: {e}") from e | |
| if not sweep: | |
| if not sweep_path.exists(): | |
| reason = "file does not exist" | |
| else: | |
| reason = "unknown reason (file exists but could not be loaded)" | |
| raise ValueError(f"Could not load sweep data from {sweep_path} ({reason})") |
| sweep = load_sweep_summary(sweep_path) | ||
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| if not sweep: | ||
| raise ValueError(f"Could not load sweep data from {sweep_path}") | ||
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Same issue: the error message "Could not load sweep data from {sweep_path}" doesn't provide the underlying reason. This appears in the update_baseline function as well.
| sweep = load_sweep_summary(sweep_path) | |
| if not sweep: | |
| raise ValueError(f"Could not load sweep data from {sweep_path}") | |
| try: | |
| sweep = load_sweep_summary(sweep_path) | |
| except Exception as e: | |
| raise ValueError(f"Could not load sweep data from {sweep_path}: {e}") from e | |
| if not sweep: | |
| if not Path(sweep_path).exists(): | |
| raise ValueError(f"Could not load sweep data from {sweep_path}: file does not exist") | |
| else: | |
| raise ValueError(f"Could not load sweep data from {sweep_path}: file is empty or invalid") |
| def test_compute_win_rate_explicit_value(self) -> None: | ||
| """Use explicit win_rate when available.""" | ||
| stats = {"avg_stability": 0.4, "win_rate": 0.8} | ||
| # The compare function checks for explicit win_rate first | ||
| # but compute_win_rate approximates from avg_stability |
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The test test_compute_win_rate_explicit_value has a misleading name and incorrect implementation. The test expects compute_win_rate to use the explicit win_rate value from the stats dictionary, but the actual implementation of compute_win_rate (lines 204-228 in manage_balance_baseline.py) only uses avg_stability and doesn't check for an explicit win_rate field. The comment on line 802 acknowledges this inconsistency. Either:
- Rename the test to reflect what it actually tests (approximation from avg_stability)
- Update
compute_win_rateto use explicit win_rate when available - Remove this test as it's testing incorrect behavior
The function compare_strategy_stats does check for explicit win_rate (lines 300-307), but compute_win_rate itself does not.
| def test_compute_win_rate_explicit_value(self) -> None: | |
| """Use explicit win_rate when available.""" | |
| stats = {"avg_stability": 0.4, "win_rate": 0.8} | |
| # The compare function checks for explicit win_rate first | |
| # but compute_win_rate approximates from avg_stability | |
| def test_compute_win_rate_ignores_explicit_value(self) -> None: | |
| """compute_win_rate ignores explicit win_rate and uses avg_stability only.""" | |
| stats = {"avg_stability": 0.4, "win_rate": 0.8} | |
| # Even though win_rate is present, compute_win_rate ignores it | |
| # and approximates from avg_stability only. |
| REGRESSION_COUNT=$(python -c "import json; d=json.load(open('build/regression_summary.json')); print(len(d.get('regressions', [])))") | ||
| if [[ "$REGRESSION_COUNT" -gt 0 ]]; then | ||
| echo "regression_detected=true" >> $GITHUB_OUTPUT | ||
| SUMMARY=$(python -c "import json; d=json.load(open('build/regression_summary.json')); print(d.get('summary', 'Regressions detected'))") |
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The Python inline commands used to extract JSON values in the bash script are brittle and hard to maintain. Consider using jq for JSON parsing in bash, which is more robust and readable. For example:
REGRESSION_COUNT=$(jq '.regressions | length' build/regression_summary.json)
SUMMARY=$(jq -r '.summary // "Regressions detected"' build/regression_summary.json)Alternatively, create a small Python helper script that handles all the output formatting to keep the logic in Python rather than mixing languages.
| REGRESSION_COUNT=$(python -c "import json; d=json.load(open('build/regression_summary.json')); print(len(d.get('regressions', [])))") | |
| if [[ "$REGRESSION_COUNT" -gt 0 ]]; then | |
| echo "regression_detected=true" >> $GITHUB_OUTPUT | |
| SUMMARY=$(python -c "import json; d=json.load(open('build/regression_summary.json')); print(d.get('summary', 'Regressions detected'))") | |
| REGRESSION_COUNT=$(jq '.regressions | length' build/regression_summary.json) | |
| if [[ "$REGRESSION_COUNT" -gt 0 ]]; then | |
| echo "regression_detected=true" >> $GITHUB_OUTPUT | |
| SUMMARY=$(jq -r '.summary // "Regressions detected"' build/regression_summary.json) |
- Updated task 11.5.1 status from not-started to COMPLETED - PR #69 merged on 2025-12-05 with CI balance validation workflow - Added .github/workflows/balance-validation.yml (285 lines) - Added scripts/manage_balance_baseline.py (948 lines) - Added tests/scripts/test_balance_validation.py (803 tests) - Baseline storage and versioning system implemented - Regression detection with configurable thresholds - Phase 11 progress: 4/6 tasks complete (67% complete) - Core balance workflow complete; 11.4.1 and 11.6.1 remain as optional - Updated next steps: prioritization decision needed for Phase 11 completion vs. Phase 12 start
.github/workflows/balance-validation.ymlcontent/config/ci_balance_sweeps.ymlcontent/baselines/balance_baseline.jsonscripts/manage_balance_baseline.pytests/scripts/test_balance_validation.py(32 tests)Original prompt
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