Validate CWICR data quality and estimate inputs. Check for errors, inconsistencies, outliers, and missing data.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Data quality issues cause:
Systematic validation of CWICR data and estimate inputs to catch errors, outliers, and inconsistencies before they impact projects.
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
class ValidationSeverity(Enum):
"""Validation issue severity."""
ERROR = "error" # Must fix
WARNING = "warning" # Should review
INFO = "info" # For awareness
class ValidationCategory(Enum):
"""Validation categories."""
MISSING_DATA = "missing_data"
INVALID_VALUE = "invalid_value"
OUTLIER = "outlier"
DUPLICATE = "duplicate"
INCONSISTENT = "inconsistent"
FORMAT = "format"
@dataclass
class ValidationIssue:
"""Single validation issue."""
field: str
record_id: str
category: ValidationCategory
severity: ValidationSeverity
message: str
current_value: Any
expected: str
@dataclass
class ValidationResult:
"""Complete validation result."""
total_records: int
valid_records: int
issues: List[ValidationIssue]
error_count: int
warning_count: int
info_count: int
validation_date: datetime
passed: bool
class CWICRDataValidator:
"""Validate CWICR data and estimates."""
# Standard validation rules
REQUIRED_FIELDS = ['work_item_code', 'description', 'unit']
NUMERIC_FIELDS = ['labor_cost', 'material_cost', 'equipment_cost', 'labor_norm']
POSITIVE_FIELDS = ['labor_cost', 'material_cost', 'equipment_cost', 'quantity']
# Outlier detection thresholds (IQR multiplier)
OUTLIER_THRESHOLD = 3.0
def __init__(self, cwicr_reference: pd.DataFrame = None):
self.reference = cwicr_reference
if cwicr_reference is not None:
self._build_reference_stats()
def _build_reference_stats(self):
"""Build reference statistics for outlier detection."""
self._stats = {}
for col in self.NUMERIC_FIELDS:
if col in self.reference.columns:
values = pd.to_numeric(self.reference[col], errors='coerce').dropna()
if len(values) > 0:
self._stats[col] = {
'mean': values.mean(),
'std': values.std(),
'q1': values.quantile(0.25),
'q3': values.quantile(0.75),
'iqr': values.quantile(0.75) - values.quantile(0.25)
}
def validate_dataframe(self, df: pd.DataFrame) -> ValidationResult:
"""Validate entire dataframe."""
issues = []
valid_count = 0
for idx, row in df.iterrows():
row_issues = self._validate_row(row, str(idx))
issues.extend(row_issues)
if not any(i.severity == ValidationSeverity.ERROR for i in row_issues):
valid_count += 1
# Check for duplicates
if 'work_item_code' in df.columns:
duplicates = df[df.duplicated(subset=['work_item_code'], keep=False)]
for idx, row in duplicates.iterrows():
issues.append(ValidationIssue(
field='work_item_code',
record_id=str(idx),
category=ValidationCategory.DUPLICATE,
severity=ValidationSeverity.WARNING,
message=f"Duplicate work item code: {row['work_item_code']}",
current_value=row['work_item_code'],
expected="Unique codes"
))
error_count = sum(1 for i in issues if i.severity == ValidationSeverity.ERROR)
warning_count = sum(1 for i in issues if i.severity == ValidationSeverity.WARNING)
info_count = sum(1 for i in issues if i.severity == ValidationSeverity.INFO)
return ValidationResult(
total_records=len(df),
valid_records=valid_count,
issues=issues,
error_count=error_count,
warning_count=warning_count,
info_count=info_count,
validation_date=datetime.now(),
passed=error_count == 0
)
def _validate_row(self, row: pd.Series, record_id: str) -> List[ValidationIssue]:
"""Validate single row."""
issues = []
# Check required fields
for field in self.REQUIRED_FIELDS:
if field in row.index:
value = row[field]
if pd.isna(value) or str(value).strip() == '':
issues.append(ValidationIssue(
field=field,
record_id=record_id,
category=ValidationCategory.MISSING_DATA,
severity=ValidationSeverity.ERROR,
message=f"Required field '{field}' is missing",
current_value=value,
expected="Non-empty value"
))
# Check numeric fields
for field in self.NUMERIC_FIELDS:
if field in row.index:
value = row[field]
if pd.notna(value):
try:
num_val = float(value)
# Check for negative where positive expected
if field in self.POSITIVE_FIELDS and num_val < 0:
issues.append(ValidationIssue(
field=field,
record_id=record_id,
category=ValidationCategory.INVALID_VALUE,
severity=ValidationSeverity.ERROR,
message=f"Negative value in '{field}'",
current_value=value,
expected="Positive number"
))
# Check for outliers
if self._stats and field in self._stats:
stats = self._stats[field]
lower = stats['q1'] - self.OUTLIER_THRESHOLD * stats['iqr']
upper = stats['q3'] + self.OUTLIER_THRESHOLD * stats['iqr']
if num_val < lower or num_val > upper:
issues.append(ValidationIssue(
field=field,
record_id=record_id,
category=ValidationCategory.OUTLIER,
severity=ValidationSeverity.WARNING,
message=f"Outlier value in '{field}'",
current_value=value,
expected=f"Between {lower:.2f} and {upper:.2f}"
))
except (ValueError, TypeError):
issues.append(ValidationIssue(
field=field,
record_id=record_id,
category=ValidationCategory.INVALID_VALUE,
severity=ValidationSeverity.ERROR,
message=f"Non-numeric value in '{field}'",
current_value=value,
expected="Numeric value"
))
# Check work item code format
if 'work_item_code' in row.index:
code = row['work_item_code']
if pd.notna(code) and not self._valid_code_format(str(code)):
issues.append(ValidationIssue(
field='work_item_code',
record_id=record_id,
category=ValidationCategory.FORMAT,
severity=ValidationSeverity.INFO,
message="Non-standard code format",
current_value=code,
expected="CATEGORY-NUMBER format"
))
return issues
def _valid_code_format(self, code: str) -> bool:
"""Check if code follows expected format."""
# Expect format like "CONC-001" or "EXCV-DEEP-002"
parts = code.split('-')
return len(parts) >= 2 and parts[0].isalpha()
def validate_estimate(self,
items: List[Dict[str, Any]],
check_against_cwicr: bool = True) -> ValidationResult:
"""Validate estimate items."""
issues = []
valid_count = 0
for i, item in enumerate(items):
record_id = str(i)
item_issues = []
# Check required fields
code = item.get('work_item_code', item.get('code'))
if not code:
item_issues.append(ValidationIssue(
field='work_item_code',
record_id=record_id,
category=ValidationCategory.MISSING_DATA,
severity=ValidationSeverity.ERROR,
message="Missing work item code",
current_value=None,
expected="Valid work item code"
))
# Check quantity
qty = item.get('quantity', 0)
if qty <= 0:
item_issues.append(ValidationIssue(
field='quantity',
record_id=record_id,
category=ValidationCategory.INVALID_VALUE,
severity=ValidationSeverity.ERROR,
message="Invalid quantity",
current_value=qty,
expected="Positive number"
))
# Check against CWICR reference
if check_against_cwicr and self.reference is not None and code:
if 'work_item_code' in self.reference.columns:
if code not in self.reference['work_item_code'].values:
item_issues.append(ValidationIssue(
field='work_item_code',
record_id=record_id,
category=ValidationCategory.INVALID_VALUE,
severity=ValidationSeverity.WARNING,
message=f"Work item code not found in CWICR: {code}",
current_value=code,
expected="Valid CWICR code"
))
issues.extend(item_issues)
if not any(i.severity == ValidationSeverity.ERROR for i in item_issues):
valid_count += 1
return ValidationResult(
total_records=len(items),
valid_records=valid_count,
issues=issues,
error_count=sum(1 for i in issues if i.severity == ValidationSeverity.ERROR),
warning_count=sum(1 for i in issues if i.severity == ValidationSeverity.WARNING),
info_count=sum(1 for i in issues if i.severity == ValidationSeverity.INFO),
validation_date=datetime.now(),
passed=all(i.severity != ValidationSeverity.ERROR for i in issues)
)
def get_data_quality_score(self, result: ValidationResult) -> Dict[str, Any]:
"""Calculate data quality score."""
if result.total_records == 0:
return {'score': 0, 'grade': 'N/A'}
# Weighted scoring
error_weight = 10
warning_weight = 3
info_weight = 1
total_deductions = (
result.error_count * error_weight +
result.warning_count * warning_weight +
result.info_count * info_weight
)
max_deductions = result.total_records * error_weight
score = max(0, 100 - (total_deductions / max_deductions * 100)) if max_deductions > 0 else 100
# Assign grade
if score >= 95:
grade = 'A'
elif score >= 85:
grade = 'B'
elif score >= 75:
grade = 'C'
elif score >= 60:
grade = 'D'
else:
grade = 'F'
return {
'score': round(score, 1),
'grade': grade,
'total_records': result.total_records,
'valid_records': result.valid_records,
'error_count': result.error_count,
'warning_count': result.warning_count
}
def export_validation_report(self,
result: ValidationResult,
output_path: str) -> str:
"""Export validation report to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
quality = self.get_data_quality_score(result)
summary_df = pd.DataFrame([{
'Total Records': result.total_records,
'Valid Records': result.valid_records,
'Errors': result.error_count,
'Warnings': result.warning_count,
'Info': result.info_count,
'Quality Score': quality['score'],
'Grade': quality['grade'],
'Validation Date': result.validation_date,
'Passed': result.passed
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Issues
if result.issues:
issues_df = pd.DataFrame([
{
'Record': i.record_id,
'Field': i.field,
'Category': i.category.value,
'Severity': i.severity.value,
'Message': i.message,
'Current Value': str(i.current_value),
'Expected': i.expected
}
for i in result.issues
])
issues_df.to_excel(writer, sheet_name='Issues', index=False)
return output_path
# Load CWICR reference
cwicr = pd.read_parquet("TR_workitems_costs_resources_DDC_CWICR.parquet")
# Initialize validator
validator = CWICRDataValidator(cwicr)
# Validate estimate
items = [
{'work_item_code': 'CONC-001', 'quantity': 100},
{'work_item_code': 'INVALID-CODE', 'quantity': -5}
]
result = validator.validate_estimate(items)
print(f"Passed: {result.passed}")
print(f"Errors: {result.error_count}")
quality = validator.get_data_quality_score(result)
print(f"Score: {quality['score']} ({quality['grade']})")
import_df = pd.read_excel("estimate_import.xlsx")
result = validator.validate_dataframe(import_df)
for issue in result.issues:
if issue.severity == ValidationSeverity.ERROR:
print(f"ERROR: {issue.message}")
validator.export_validation_report(result, "validation_report.xlsx")
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
日本語の概要は準備中です。原文の説明を表示しています。
Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.
日本語の概要は準備中です。原文の説明を表示しています。
Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.
日本語の概要は準備中です。原文の説明を表示しています。