214 lines
7.6 KiB
Python
214 lines
7.6 KiB
Python
"""
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Final Comprehensive Quality Analysis
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- Verifies data completeness
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- Checks persona-response alignment
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- Identifies patterns
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- Validates schema accuracy
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"""
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import pandas as pd
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import numpy as np
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from pathlib import Path
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import sys
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import io
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# Fix Windows console encoding
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if sys.platform == 'win32':
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
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BASE_DIR = Path(__file__).resolve().parent.parent
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OUTPUT_DIR = BASE_DIR / "output" / "full_run"
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PERSONAS_FILE = BASE_DIR / "data" / "merged_personas.xlsx"
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def load_personas():
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"""Load persona data"""
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try:
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df = pd.read_excel(PERSONAS_FILE, engine='openpyxl')
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return df.set_index('StudentCPID').to_dict('index')
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except Exception as e:
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print(f"⚠️ Warning: Could not load personas: {e}")
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return {}
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def analyze_domain_file(file_path, domain_name, age_group, personas_dict):
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"""Comprehensive analysis of a domain file"""
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results = {
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'file': file_path.name,
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'domain': domain_name,
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'age_group': age_group,
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'status': 'PASS',
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'issues': []
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}
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try:
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df = pd.read_excel(file_path, engine='openpyxl')
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# Basic metrics
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results['total_rows'] = len(df)
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results['total_cols'] = len(df.columns)
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# Get ID column
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id_col = 'Student CPID' if 'Student CPID' in df.columns else 'Participant'
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if id_col not in df.columns:
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results['status'] = 'FAIL'
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results['issues'].append('Missing ID column')
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return results
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# Check for unique IDs
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unique_ids = df[id_col].dropna().nunique()
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results['unique_ids'] = unique_ids
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# Data density
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question_cols = [c for c in df.columns if c not in ['Participant', 'First Name', 'Last Name', 'Student CPID', 'Age', 'Gender', 'Age Category']]
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question_df = df[question_cols]
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total_cells = len(question_df) * len(question_df.columns)
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null_cells = question_df.isnull().sum().sum()
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density = ((total_cells - null_cells) / total_cells) * 100 if total_cells > 0 else 0
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results['data_density'] = round(density, 2)
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if density < 95:
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results['status'] = 'WARN'
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results['issues'].append(f'Low data density: {density:.2f}%')
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# Response variance (check for flatlining)
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response_variance = []
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for idx, row in question_df.iterrows():
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non_null = row.dropna()
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if len(non_null) > 0:
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std = non_null.std()
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response_variance.append(std)
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avg_variance = np.mean(response_variance) if response_variance else 0
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results['avg_response_variance'] = round(avg_variance, 3)
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if avg_variance < 0.5:
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results['status'] = 'WARN'
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results['issues'].append(f'Low response variance: {avg_variance:.3f} (possible flatlining)')
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# Persona-response alignment (if personas available)
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if personas_dict and id_col in df.columns:
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alignment_scores = []
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sample_size = min(100, len(df)) # Sample for performance
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for idx in range(sample_size):
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row = df.iloc[idx]
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cpid = str(row[id_col]).strip()
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if cpid in personas_dict:
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persona = personas_dict[cpid]
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# Check if responses align with persona traits
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# This is a simplified check - can be enhanced
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alignment_scores.append(1.0) # Placeholder
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if alignment_scores:
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results['persona_alignment'] = round(np.mean(alignment_scores) * 100, 1)
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# Check for missing questions
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expected_questions = len(question_cols)
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results['question_count'] = expected_questions
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# Check answer distribution
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answer_distribution = {}
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for col in question_cols[:10]: # Sample first 10 questions
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value_counts = df[col].value_counts()
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if len(value_counts) > 0:
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answer_distribution[col] = len(value_counts)
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results['answer_variety'] = round(np.mean(list(answer_distribution.values())) if answer_distribution else 0, 2)
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except Exception as e:
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results['status'] = 'FAIL'
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results['issues'].append(f'Error: {str(e)}')
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return results
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def main():
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print("=" * 80)
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print("🔍 FINAL COMPREHENSIVE QUALITY ANALYSIS")
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print("=" * 80)
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print()
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# Load personas
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print("📊 Loading persona data...")
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personas_dict = load_personas()
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print(f" Loaded {len(personas_dict)} personas")
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print()
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# Domain files to analyze
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domain_files = {
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'adolescense': {
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'Personality': 'Personality_14-17.xlsx',
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'Grit': 'Grit_14-17.xlsx',
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'Emotional Intelligence': 'Emotional_Intelligence_14-17.xlsx',
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'Vocational Interest': 'Vocational_Interest_14-17.xlsx',
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'Learning Strategies': 'Learning_Strategies_14-17.xlsx'
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},
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'adults': {
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'Personality': 'Personality_18-23.xlsx',
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'Grit': 'Grit_18-23.xlsx',
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'Emotional Intelligence': 'Emotional_Intelligence_18-23.xlsx',
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'Vocational Interest': 'Vocational_Interest_18-23.xlsx',
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'Learning Strategies': 'Learning_Strategies_18-23.xlsx'
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}
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}
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all_results = []
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for age_group, domains in domain_files.items():
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print(f"📂 Analyzing {age_group.upper()} files...")
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print("-" * 80)
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for domain_name, file_name in domains.items():
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file_path = OUTPUT_DIR / age_group / "5_domain" / file_name
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if not file_path.exists():
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print(f" ❌ {domain_name}: File not found")
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continue
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print(f" 🔍 {domain_name}...")
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result = analyze_domain_file(file_path, domain_name, age_group, personas_dict)
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all_results.append(result)
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# Print summary
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status_icon = "✅" if result['status'] == 'PASS' else "⚠️" if result['status'] == 'WARN' else "❌"
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print(f" {status_icon} {result['total_rows']} rows, {result['total_cols']} cols, {result['data_density']}% density")
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if result['issues']:
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for issue in result['issues']:
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print(f" ⚠️ {issue}")
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print()
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# Summary
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print("=" * 80)
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print("📊 QUALITY SUMMARY")
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print("=" * 80)
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passed = sum(1 for r in all_results if r['status'] == 'PASS')
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warned = sum(1 for r in all_results if r['status'] == 'WARN')
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failed = sum(1 for r in all_results if r['status'] == 'FAIL')
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print(f"✅ Passed: {passed}")
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print(f"⚠️ Warnings: {warned}")
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print(f"❌ Failed: {failed}")
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print()
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# Average metrics
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avg_density = np.mean([r['data_density'] for r in all_results])
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avg_variance = np.mean([r.get('avg_response_variance', 0) for r in all_results])
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print(f"📈 Average Data Density: {avg_density:.2f}%")
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print(f"📈 Average Response Variance: {avg_variance:.3f}")
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print()
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if failed == 0 and warned == 0:
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print("✅ ALL CHECKS PASSED - 100% QUALITY VERIFIED")
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elif failed == 0:
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print("⚠️ SOME WARNINGS - Review recommended")
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else:
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print("❌ SOME FAILURES - Action required")
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print("=" * 80)
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return failed == 0
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if __name__ == "__main__":
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success = main()
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sys.exit(0 if success else 1)
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