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"""
Data Quality Validator for Stock Predictor
Validates data quality across all data sources and features
Usage:
python data_quality_validator.py [--check all|features|stock_data|predictions]
python data_quality_validator.py --report
"""
import os
import sys
import json
from pathlib import Path
from datetime import datetime
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, field, asdict
import pandas as pd
import numpy as np
# Add project root to path
PROJECT_DIR = Path(__file__).parent.parent
sys.path.insert(0, str(PROJECT_DIR))
@dataclass
class ValidationResult:
"""Data quality validation result"""
check_name: str
passed: bool
message: str
details: List[str] = field(default_factory=list)
severity: str = "info" # info, warning, error
timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
class DataQualityValidator:
"""Data quality validator for stock predictor"""
def __init__(self, project_dir: Path = PROJECT_DIR):
self.project_dir = project_dir
self.data_dir = project_dir / "data"
self.results: List[ValidationResult] = []
def add_result(self, result: ValidationResult):
"""Add validation result"""
self.results.append(result)
status = "✓" if result.passed else "✗"
severity = result.severity.upper()
print(f"{status} [{severity}] {result.check_name}: {result.message}")
if result.details:
for detail in result.details[:3]: # Show first 3 details
print(f" - {detail}")
def validate_json_structure(self, filepath: Path, required_keys: List[str]) -> ValidationResult:
"""Validate JSON file structure"""
try:
with open(filepath, 'r', encoding='utf-8') as f:
data = json.load(f)
missing_keys = [k for k in required_keys if k not in data]
if missing_keys:
return ValidationResult(
check_name=f"JSON structure: {filepath.name}",
passed=False,
message=f"Missing required keys: {missing_keys}",
details=[f"File: {filepath}"],
severity="error"
)
return ValidationResult(
check_name=f"JSON structure: {filepath.name}",
passed=True,
message="All required keys present",
details=[f"File: {filepath}, Keys: {len(data.keys())}"],
severity="info"
)
except json.JSONDecodeError as e:
return ValidationResult(
check_name=f"JSON structure: {filepath.name}",
passed=False,
message=f"Invalid JSON: {e}",
details=[f"File: {filepath}"],
severity="error"
)
except Exception as e:
return ValidationResult(
check_name=f"JSON structure: {filepath.name}",
passed=False,
message=f"Cannot read file: {e}",
details=[f"File: {filepath}"],
severity="error"
)
def validate_stock_data(self) -> List[ValidationResult]:
"""Validate stock data files"""
results = []
# Check TAIEX data
taifex_path = self.data_dir / "taifex" / "daily"
if taifex_path.exists():
files = list(taifex_path.glob("*.csv"))
if files:
latest_file = max(files, key=lambda f: f.stat().st_mtime)
try:
df = pd.read_csv(latest_file)
if df.shape[0] > 0:
results.append(ValidationResult(
check_name="TAIEX daily data",
passed=True,
message=f"Loaded {df.shape[0]} rows, {df.shape[1]} columns",
details=[f"Latest file: {latest_file.name}"],
severity="info"
))
else:
results.append(ValidationResult(
check_name="TAIEX daily data",
passed=False,
message="Empty dataframe",
details=[f"File: {latest_file.name}"],
severity="warning"
))
except Exception as e:
results.append(ValidationResult(
check_name="TAIEX daily data",
passed=False,
message=f"Cannot read CSV: {e}",
details=[f"File: {latest_file.name}"],
severity="error"
))
else:
results.append(ValidationResult(
check_name="TAIEX daily data",
passed=False,
message="No CSV files found",
details=[f"Directory: {taifex_path}"],
severity="warning"
))
return results
def validate_predictions(self) -> List[ValidationResult]:
"""Validate prediction files"""
results = []
# Check precomputed predictions
predictions_path = self.data_dir / "precomputed_predictions.json"
if predictions_path.exists():
result = self.validate_json_structure(predictions_path, [])
result.details.append(f"Size: {predictions_path.stat().st_size / 1024:.1f} KB")
results.append(result)
else:
results.append(ValidationResult(
check_name="Precomputed predictions",
passed=False,
message="File not found",
details=[f"Path: {predictions_path}"],
severity="warning"
))
return results
def validate_query_history(self) -> List[ValidationResult]:
"""Validate query history file"""
results = []
history_path = self.data_dir / "query_history.json"
if history_path.exists():
result = self.validate_json_structure(history_path, ["predictions"])
if result.passed:
with open(history_path, 'r', encoding='utf-8') as f:
data = json.load(f)
if "predictions" in data:
results.append(ValidationResult(
check_name="Query history predictions",
passed=True,
message=f"Contains {len(data['predictions'])} predictions",
details=[f"File: {history_path}"],
severity="info"
))
return results
def validate_feature_columns(self) -> List[ValidationResult]:
"""Validate feature columns in models"""
results = []
try:
# Import the predictor module
from models.predictor import FEATURE_COLUMNS, FEATURE_VERSION
# Check if features are defined
if FEATURE_COLUMNS:
results.append(ValidationResult(
check_name="Feature columns defined",
passed=True,
message=f"{len(FEATURE_COLUMNS)} features defined (v{FEATURE_VERSION})",
details=[f"Feature count: {len(FEATURE_COLUMNS)}", f"Version: {FEATURE_VERSION}"],
severity="info"
))
else:
results.append(ValidationResult(
check_name="Feature columns defined",
passed=False,
message="No features defined",
details=[],
severity="error"
))
except Exception as e:
results.append(ValidationResult(
check_name="Feature columns defined",
passed=False,
message=f"Cannot import features: {e}",
details=[],
severity="error"
))
return results
def generate_report(self, output_path: Optional[Path] = None) -> Dict[str, Any]:
"""Generate validation report"""
# Calculate summary
total = len(self.results)
passed = sum(1 for r in self.results if r.passed)
failed = total - passed
# Group by severity
by_severity = {"info": 0, "warning": 0, "error": 0}
for r in self.results:
by_severity[r.severity] += 1
report = {
"timestamp": datetime.now().isoformat(),
"summary": {
"total_checks": total,
"passed": passed,
"failed": failed,
"by_severity": by_severity
},
"results": [asdict(r) for r in self.results]
}
if output_path:
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(report, f, indent=2, ensure_ascii=False)
return report
def main():
"""Main function"""
import argparse
parser = argparse.ArgumentParser(description="Data Quality Validator")
parser.add_argument(
"--check",
choices=["all", "features", "stock_data", "predictions", "query_history"],
default="all",
help="Check type"
)
parser.add_argument(
"--report",
action="store_true",
help="Generate detailed report"
)
parser.add_argument(
"--output",
help="Output path for report"
)
args = parser.parse_args()
validator = DataQualityValidator()
if args.check == "features":
results = validator.validate_feature_columns()
elif args.check == "stock_data":
results = validator.validate_stock_data()
elif args.check == "predictions":
results = validator.validate_predictions()
elif args.check == "query_history":
results = validator.validate_query_history()
else: # all
results = (
validator.validate_feature_columns() +
validator.validate_stock_data() +
validator.validate_predictions() +
validator.validate_query_history()
)
for r in results:
validator.add_result(r)
# Generate report if requested
if args.report:
report = validator.generate_report()
if args.output:
output_path = Path(args.output)
output_path.parent.mkdir(parents=True, exist_ok=True)
else:
output_path = PROJECT_DIR / "docs" / "data_quality_report.json"
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(report, f, indent=2, ensure_ascii=False)
print(f"\n✓ Report saved to: {output_path}")
print(f"\nSummary:")
print(f" Total checks: {report['summary']['total_checks']}")
print(f" Passed: {report['summary']['passed']}")
print(f" Failed: {report['summary']['failed']}")
print(f" Errors: {report['summary']['by_severity']['error']}")
print(f" Warnings: {report['summary']['by_severity']['warning']}")
print(f" Info: {report['summary']['by_severity']['info']}")
# Exit with error code if any errors
exit_code = 0
for r in validator.results:
if r.severity == "error" and not r.passed:
exit_code = 1
break
return exit_code
if __name__ == "__main__":
exit(main())
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