Feature/ticket 16 Add Data Validation and Quality Scoring for Fixtures and Teams#28
Feature/ticket 16 Add Data Validation and Quality Scoring for Fixtures and Teams#28lizaj99 wants to merge 6 commits into
Conversation
- Added validate_fixture() method with required field validation - Enhanced validate_team_data() with type checking and name validation - Updated score_game_data() to penalize missing/invalid fields - Integrated logging for all validation issues - Added comprehensive test coverage for validation scenarios - Quality scoring (0-100) for data completeness assessment - Graceful handling of validation failures with detailed issue reporting Closes vibing-ai#16
WalkthroughComprehensive enhancements were made to the data validation and cleaning logic for sports fixtures and teams. A new, detailed test suite was introduced to cover various scenarios, including edge cases and real-world API data. The data validation module now features granular checks, scoring, and improved cleaning methods for team names, player names, dates, and numeric statistics. Changes
Sequence Diagram(s)sequenceDiagram
participant TestSuite
participant DataValidator
participant DataCleaner
TestSuite->>DataValidator: validate_fixture(fixture)
DataValidator->>DataValidator: _check_fixture_sections()
DataValidator->>DataValidator: _check_team_data()
DataValidator->>DataValidator: _check_score_format()
DataValidator->>DataValidator: _check_date_format()
DataValidator->>DataValidator: _check_for_negative_scores()
DataValidator-->>TestSuite: (is_valid, score, issues)
TestSuite->>DataCleaner: clean_team_name(name)
DataCleaner-->>TestSuite: cleaned_name
TestSuite->>DataCleaner: normalize_date(date)
DataCleaner-->>TestSuite: normalized_date
TestSuite->>DataCleaner: clean_numeric_stats(stats)
DataCleaner-->>TestSuite: cleaned_stats
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Poem
Note ⚡️ Unit Test Generation is now available in beta!Learn more here, or try it out under "Finishing Touches" below. 📜 Recent review detailsConfiguration used: CodeRabbit UI 📒 Files selected for processing (1)
🚧 Files skipped from review as they are similar to previous changes (1)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
✨ Finishing Touches
🧪 Generate unit tests
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
SupportNeed help? Create a ticket on our support page for assistance with any issues or questions. Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
CodeRabbit Configuration File (
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 2
🧹 Nitpick comments (2)
ai-backend/tests/test_validation.py (2)
13-41: Consider adding assertions for automated testingWhile the manual output is helpful for debugging, consider adding assertions to make this a proper unit test that can fail in CI/CD pipelines.
Example assertion:
assert valid == True, f"Expected valid fixture but got {valid}" assert score == 100, f"Expected score 100 but got {score}"
299-310: Consider adding test summary outputThe test orchestration is well-structured. Consider adding a summary at the end showing total tests run and any failures detected.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (2)
ai-backend/tests/test_validation.py(1 hunks)ai-backend/tools/data_validation.py(2 hunks)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Codacy Static Code Analysis
🔇 Additional comments (9)
ai-backend/tools/data_validation.py (5)
52-72: Well-structured validation methodThe implementation correctly validates fixtures with proper error tracking and score calculation. The use of
max(0, score_value)ensures scores don't go negative.
44-47: Inconsistent score field naming between validation methodsThe
validate_game_datamethod expects score inscore.ftformat (line 44), whilevalidate_fixturealso usesscore.ft(line 65), butvalidate_api_football_fixtureexpectsscore.fulltimeformat (line 104). This inconsistency could lead to confusion when using different validation methods.Consider standardizing the expected score format or documenting the different formats expected by each method.
Also applies to: 65-67, 103-106
133-139: Good type validation additionsThe added type checks for
team_idandnameimprove data integrity validation.
179-230: Well-implemented data cleaning methodsThe cleaning methods handle various edge cases effectively:
- Proper handling of empty/None values
- Multiple date format support
- Safe numeric conversion with error logging
34-36: Potential AttributeError when team names are NoneThe code calls
strip()on the result ofgame_data.get(key), which could beNoneif the key doesn't exist, causing an AttributeError.Apply this diff to handle None values safely:
-if not all(isinstance(game_data.get(key), str) and game_data[key].strip() for key in ["home_team", "away_team"]): +if not all(isinstance(game_data.get(key), str) and game_data.get(key, "").strip() for key in ["home_team", "away_team"]):Likely an incorrect or invalid review comment.
ai-backend/tests/test_validation.py (4)
67-81: Good coverage of cleaning methodsThe test covers all cleaning methods with appropriate test cases including edge cases.
83-178: Well-structured API validation testsGood implementation with:
- Proper async/await usage
- API key availability check
- Realistic test data matching API-Football format
180-243: Excellent test coverage for problematic scenariosComprehensive test cases covering:
- Missing sections
- Invalid formats
- Empty values
- Type mismatches
- Invalid data (negative scores)
The verification logic ensures the validator correctly identifies issues.
245-297: Thorough edge case testing with verificationGood test design with:
- Clear expected outcomes
- Automated pass/fail verification
- Coverage of boundary conditions
| from tools.sports_apis import APIFootballClient | ||
|
|
||
| load_dotenv() | ||
| print("API Key from env:", os.getenv("API_FOOTBALL_KEY")) |
There was a problem hiding this comment.
Remove API key from console output
Printing API keys to console poses a security risk as they could be exposed in logs or CI/CD outputs.
Apply this diff to remove the API key print:
-print("API Key from env:", os.getenv("API_FOOTBALL_KEY"))
+# Verify API key is loaded without printing it
+api_key_loaded = bool(os.getenv("API_FOOTBALL_KEY"))
+print(f"API Key loaded: {api_key_loaded}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| print("API Key from env:", os.getenv("API_FOOTBALL_KEY")) | |
| @@ ai-backend/tests/test_validation.py | |
| - print("API Key from env:", os.getenv("API_FOOTBALL_KEY")) | |
| + # Verify API key is loaded without printing it | |
| + api_key_loaded = bool(os.getenv("API_FOOTBALL_KEY")) | |
| + print(f"API Key loaded: {api_key_loaded}") |
🤖 Prompt for AI Agents
In ai-backend/tests/test_validation.py at line 10, remove the print statement
that outputs the API key from the environment variable to avoid exposing
sensitive information in logs or CI/CD outputs. Simply delete or comment out the
line containing the print statement.
| try: | ||
| datetime.strptime(game_data.get("date", ""), "%Y-%m-%d") | ||
| except Exception: | ||
| score -= 10 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Avoid bare except clause
The bare except Exception: clause could hide unexpected errors. Consider catching specific exceptions.
Apply this diff to catch specific exceptions:
-except Exception:
+except (ValueError, TypeError):📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try: | |
| datetime.strptime(game_data.get("date", ""), "%Y-%m-%d") | |
| except Exception: | |
| score -= 10 | |
| try: | |
| datetime.strptime(game_data.get("date", ""), "%Y-%m-%d") | |
| except (ValueError, TypeError): | |
| score -= 10 |
🤖 Prompt for AI Agents
In ai-backend/tools/data_validation.py around lines 163 to 166, replace the bare
except clause catching all exceptions with a more specific exception handler.
Change the except block to catch only the exceptions that datetime.strptime can
raise, such as ValueError, to avoid hiding unexpected errors. This will make
error handling more precise and safer.
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Nitpick comments (4)
ai-backend/tests/test_validation.py (4)
13-41: Consider adding automated assertions for proper unit testing.The test structure and data examples are good, covering both valid and invalid fixture scenarios. However, this appears to be more of a manual verification test since it only prints results without automated assertions.
Consider adding assertions to make this a proper unit test:
for name, fixture in { "Good Fixture": fixture_good, "Bad Fixture": fixture_missing_fields }.items(): print(f"\n{name}:") valid, score, issues = DataValidator.validate_fixture(fixture) print(f"Valid: {valid}") print(f"Quality Score: {score}") print(f"Issues: {issues}") + + # Add assertions for automated testing + if name == "Good Fixture": + assert valid == True, f"Expected valid fixture but got {valid}" + assert score > 80, f"Expected high quality score but got {score}" + assert len(issues) == 0, f"Expected no issues but got {issues}" + elif name == "Bad Fixture": + assert valid == False, f"Expected invalid fixture but got {valid}" + assert score < 50, f"Expected low quality score but got {score}" + assert len(issues) > 0, f"Expected issues but got none"
43-65: Consider API consistency and add automated assertions.The test covers valid and invalid team scenarios appropriately. However, I notice that
validate_team_data()only returns a boolean whilevalidate_fixture()returns a tuple with validity, score, and issues. Consider standardizing the validation API for consistency.Add assertions and consider requesting API consistency:
for name, team in { "Good Team": team_good, "Bad Team": team_bad }.items(): print(f"\n{name}:") valid = DataValidator.validate_team_data(team) print(f"Valid: {valid}") + + # Add assertions + if name == "Good Team": + assert valid == True, f"Expected valid team but got {valid}" + elif name == "Bad Team": + assert valid == False, f"Expected invalid team but got {valid}"
67-81: Add assertions to verify cleaning behavior.The test covers the main cleaning methods with good edge case data. However, automated assertions would make this more robust.
Add assertions to verify expected cleaning behavior:
print("Cleaned team name:", DataCleaner.clean_team_name("Liverpool FC")) +assert DataCleaner.clean_team_name("Liverpool FC") == "Liverpool FC" + print("Cleaned player name:", DataCleaner.clean_player_name("john smith jr.")) +cleaned_player = DataCleaner.clean_player_name("john smith jr.") +assert cleaned_player == "John Smith Jr." or cleaned_player.title() in cleaned_player + print("Normalized date:", DataCleaner.normalize_date("May 25, 2025")) +normalized_date = DataCleaner.normalize_date("May 25, 2025") +assert normalized_date is not None and len(normalized_date) > 0 + stats = { "goals": " 2 ", "xG": "1.23", "yellow_cards": None, "invalid": "N/A" } -print("Cleaned stats:", DataCleaner.clean_numeric_stats(stats)) +cleaned_stats = DataCleaner.clean_numeric_stats(stats) +print("Cleaned stats:", cleaned_stats) +assert cleaned_stats["goals"] == 2 +assert cleaned_stats["xG"] == 1.23 +assert "yellow_cards" not in cleaned_stats or cleaned_stats["yellow_cards"] is None
180-243: Consider refactoring for better maintainability.Excellent coverage of problematic data scenarios with comprehensive test cases. The verification logic ensures problematic data is correctly identified. However, the function could be broken down for better maintainability.
Consider extracting the test data to improve readability:
+def _get_problematic_api_fixtures(): + """Get test data for problematic API-Football scenarios""" + return [ + { + "name": "Missing Fixture Section", + "data": { + "league": {"id": 39, "name": "Premier League"}, + # ... rest of test data + } + }, + # ... other test cases + ] + def test_api_football_problematic_data(): """Test validation against problematic API-Football data scenarios""" print("\n=== Test API-Football Problematic Data Scenarios ===") - problematic_api_fixtures = [ - # Move large test data array to separate function - ] + problematic_api_fixtures = _get_problematic_api_fixtures() for scenario in problematic_api_fixtures: # ... rest of function unchanged
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
ai-backend/tests/test_validation.py(1 hunks)
🧰 Additional context used
🪛 GitHub Check: Codacy Static Code Analysis
ai-backend/tests/test_validation.py
[warning] 83-83: ai-backend/tests/test_validation.py#L83
Method test_real_api_football_data has 83 lines of code (limit is 50)
[warning] 180-180: ai-backend/tests/test_validation.py#L180
Method test_api_football_problematic_data has 58 lines of code (limit is 50)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Codacy Static Code Analysis
🔇 Additional comments (3)
ai-backend/tests/test_validation.py (3)
1-11: LGTM! Security issue appears resolved.The imports and environment setup look good. The previous security concern about printing the API key to console appears to have been addressed as the problematic print statement is no longer present.
245-297: Excellent test structure and practices!This function demonstrates proper unit testing with expected results, automated verification, and clear pass/fail indicators. The edge cases are well-chosen and the structure could serve as a model for the other test functions.
299-311: LGTM! Well-organized test execution.The main function properly orchestrates all tests with correct handling of both synchronous and asynchronous test functions. The execution order is logical and follows good practices.
There was a problem hiding this comment.
Actionable comments posted: 1
♻️ Duplicate comments (1)
ai-backend/tools/data_validation.py (1)
168-171: Avoid bare except clause.The bare
except Exception:clause could hide unexpected errors. This duplicates a previous review comment.Apply this diff to catch specific exceptions:
- except Exception: + except (ValueError, TypeError):
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
ai-backend/tools/data_validation.py(2 hunks)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Codacy Static Code Analysis
🔇 Additional comments (7)
ai-backend/tools/data_validation.py (7)
22-49: Excellent validation enhancements with proper type checking.The method now includes comprehensive validation with explicit type checking for all key fields. Good practices include:
- Proper exception handling for date parsing (ValueError)
- Thorough validation of score structure
- Clear logging messages for each validation failure
52-72: Well-implemented fixture validation with quality scoring.The method provides comprehensive validation with detailed issue tracking and a sensible scoring mechanism. The 20-point deduction per issue and minimum score capping at 0 creates a fair quality assessment system.
75-87: Good modular validation design with comprehensive checks.The method delegates validation to well-structured helper methods, providing comprehensive validation for API-Football data structures. The 5-point deduction system offers more granular scoring than other methods.
88-127: Well-structured helper methods with appropriate validation logic.The helper methods provide modular validation covering all essential aspects:
- Missing sections validation
- Team data completeness
- Score format validation with timezone-aware dates
- Business rule validation (negative scores)
129-146: Enhanced team validation with proper type enforcement.The method now includes explicit type checking for
team_id(integer) and validatesnameas a non-empty string, improving data quality assurance while maintaining existing field validation.
159-176: Good scoring logic but address the bare except clause.The point deduction system is well-designed with appropriate penalties for missing critical fields (20 points) versus format issues (10 points). The minimum score capping prevents negative values.
184-235: Excellent enhancements to data cleaning methods.The cleaning methods now include:
- Football-specific suffix removal for team names
- Proper title casing for player names
- Comprehensive date format support
- Robust numeric stats cleaning that properly handles None values and logs failures
The error handling is well-implemented, avoiding silent failures and providing appropriate logging.
| @staticmethod | ||
| @staticmethod |
There was a problem hiding this comment.
Remove duplicate @staticmethod decorator.
Line 117 contains a duplicate @staticmethod decorator that should be removed.
- @staticmethod
- @staticmethod
+ @staticmethod📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| @staticmethod | |
| @staticmethod | |
| @staticmethod |
🤖 Prompt for AI Agents
In ai-backend/tools/data_validation.py at lines 116 to 117, there is a duplicate
@staticmethod decorator applied to the same method. Remove the redundant
@staticmethod decorator so that only one remains above the method definition.
There was a problem hiding this comment.
Actionable comments posted: 0
🧹 Nitpick comments (2)
ai-backend/tests/test_validation.py (2)
84-139: Consider further refactoring for better maintainability.The function is well-structured and correctly handles API key checking without exposure. However, it could benefit from extracting the fixture data creation into a separate helper function to improve readability and maintainability.
Consider this refactoring:
+def _get_sample_api_fixtures(): + """Return sample API-Football format fixtures for testing""" + return [ + { + "fixture": { + "id": 1234567, + "date": "2024-08-16T20:00:00+00:00", + "timestamp": 1723833600 + }, + # ... rest of fixture data + }, + # ... second fixture + ] async def test_real_api_football_data(): print("\n=== Test Real API-Football Data Validation ===") api_key = os.getenv("API_FOOTBALL_KEY") if not api_key: print("⚠️ API_FOOTBALL_KEY not found in environment, skipping real API tests") return try: async with APIFootballClient(): print("Testing validation with API-Football format data...") - real_fixtures = [ - # ... long fixture data - ] + real_fixtures = _get_sample_api_fixtures()
151-164: Comprehensive test coverage with opportunity for improvement.Excellent coverage of problematic API data scenarios including missing sections, invalid formats, and edge cases. The test cases are well-chosen and realistic.
Consider extracting the test data to improve readability:
+def _get_problematic_api_fixtures(): + """Return problematic API-Football format fixtures for testing""" + return [ + { + "name": "Missing Fixture Section", + "data": { + "league": {"id": 39, "name": "Premier League"}, + "teams": {"home": {"id": 33, "name": "Team A"}, "away": {"id": 36, "name": "Team B"}}, + "score": {"fulltime": {"home": 1, "away": 0}} + } + }, + # ... other cases + ] def test_api_football_problematic_data(): print("\n=== Test API-Football Problematic Data Scenarios ===") - problematic_api_fixtures = [ - # ... long inline data - ] + problematic_api_fixtures = _get_problematic_api_fixtures()
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
ai-backend/tests/test_validation.py(1 hunks)
🧰 Additional context used
🪛 GitHub Check: Codacy Static Code Analysis
ai-backend/tests/test_validation.py
[warning] 84-84: ai-backend/tests/test_validation.py#L84
Method test_real_api_football_data has 83 lines of code (limit is 50)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: Codacy Static Code Analysis
🔇 Additional comments (8)
ai-backend/tests/test_validation.py (8)
1-10: LGTM!The imports are well-organized and the dotenv setup is appropriate for loading environment variables needed for API testing.
11-37: LGTM!Good test coverage with both valid and invalid fixture scenarios. The test data is realistic and the output format clearly shows validation results.
38-58: LGTM!Good test coverage for team validation with both valid and invalid scenarios. The test correctly handles the different return signature of
validate_team_datacompared tovalidate_fixture.
59-73: LGTM!Comprehensive testing of data cleaning methods with good edge case coverage including spaces, None values, and invalid data formats.
74-83: LGTM!Good refactoring to extract this helper function, which improves maintainability and addresses the previous feedback about function length. The nested structure access is handled correctly.
140-150: LGTM!Well-designed helper function with clear success/failure verification logic. The validation that problematic data is correctly identified (both
valid=Falseandscore<100) is sound.
165-184: LGTM!Excellent test design with explicit expected outcomes and proper result validation. The edge cases cover the full spectrum from perfect to highly problematic data, and the pass/fail verification is robust.
185-195: LGTM!Clean orchestration function with logical test execution order and proper async handling.
1 participant
Title:
AI-016: Add Data Validation and Quality Scoring for Fixtures and Teams
Summary
This pull request introduces a validation framework to ensure the completeness and correctness of football fixture and team data. It verifies required fields, checks for sane values, assigns quality scores, and logs any issues encountered during validation.
Key Changes
tools/data_validation.pyAdded
validate_fixture()methodfixture_id,round,team1,team2,date,score.ftis_valid(bool),quality_score(int),issues(list[str])Enhanced
validate_team_data()team_idis an integernameis a non-empty stringRefactored
score_game_data()Logging
logger.warning()tests/test_validation.pyFixture Validation Tests
Team Validation Tests
Data Cleaning Tests
Real API-Football Fixture Tests
Edge Case Tests
✅ Acceptance Criteria Met