mirror of
https://github.com/PlatypusPus/MushroomEmpire.git
synced 2026-02-07 22:18:59 +00:00
Feat:Create the Basic Ai Governance Package to use has a guide
This commit is contained in:
145
src/__init__.py
Normal file
145
src/__init__.py
Normal file
@@ -0,0 +1,145 @@
|
||||
"""
|
||||
AI Governance Module - Bias Detection and Risk Analysis
|
||||
"""
|
||||
|
||||
from .data_processor import DataProcessor
|
||||
from .model_trainer import GeneralizedModelTrainer
|
||||
from .bias_analyzer import BiasAnalyzer
|
||||
from .risk_analyzer import RiskAnalyzer
|
||||
from .report_generator import ReportGenerator, NumpyEncoder
|
||||
|
||||
import pandas as pd
|
||||
import json
|
||||
|
||||
__version__ = '1.0.0'
|
||||
|
||||
__all__ = [
|
||||
'DataProcessor',
|
||||
'GeneralizedModelTrainer',
|
||||
'BiasAnalyzer',
|
||||
'RiskAnalyzer',
|
||||
'ReportGenerator',
|
||||
'NumpyEncoder',
|
||||
'AIGovernanceAnalyzer'
|
||||
]
|
||||
|
||||
|
||||
class AIGovernanceAnalyzer:
|
||||
"""
|
||||
Main interface for AI Governance analysis
|
||||
|
||||
Example:
|
||||
>>> analyzer = AIGovernanceAnalyzer()
|
||||
>>> report = analyzer.analyze('data.csv', 'target', ['gender', 'age'])
|
||||
>>> print(f"Bias Score: {report['summary']['overall_bias_score']:.3f}")
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the analyzer"""
|
||||
self.processor = None
|
||||
self.trainer = None
|
||||
self.bias_analyzer = None
|
||||
self.risk_analyzer = None
|
||||
self.report_generator = None
|
||||
|
||||
def analyze(self, data_path, target_column, protected_attributes):
|
||||
"""
|
||||
Run complete AI governance analysis from file
|
||||
|
||||
Args:
|
||||
data_path (str): Path to CSV file
|
||||
target_column (str): Name of target column
|
||||
protected_attributes (list): List of protected attribute column names
|
||||
|
||||
Returns:
|
||||
dict: Complete analysis report
|
||||
"""
|
||||
df = pd.read_csv(data_path)
|
||||
return self.analyze_dataframe(df, target_column, protected_attributes)
|
||||
|
||||
def analyze_dataframe(self, df, target_column, protected_attributes):
|
||||
"""
|
||||
Run complete AI governance analysis from DataFrame
|
||||
|
||||
Args:
|
||||
df (pd.DataFrame): Input dataframe
|
||||
target_column (str): Name of target column
|
||||
protected_attributes (list): List of protected attribute column names
|
||||
|
||||
Returns:
|
||||
dict: Complete analysis report
|
||||
"""
|
||||
# Step 1: Process data
|
||||
self.processor = DataProcessor(df)
|
||||
self.processor.target_column = target_column
|
||||
self.processor.protected_attributes = protected_attributes
|
||||
self.processor.prepare_data()
|
||||
|
||||
# Step 2: Train model
|
||||
self.trainer = GeneralizedModelTrainer(
|
||||
self.processor.X_train,
|
||||
self.processor.X_test,
|
||||
self.processor.y_train,
|
||||
self.processor.y_test,
|
||||
self.processor.feature_names
|
||||
)
|
||||
self.trainer.train()
|
||||
self.trainer.evaluate()
|
||||
|
||||
# Step 3: Analyze bias
|
||||
self.bias_analyzer = BiasAnalyzer(
|
||||
self.processor.X_test,
|
||||
self.processor.y_test,
|
||||
self.trainer.y_pred,
|
||||
self.processor.df,
|
||||
self.processor.protected_attributes,
|
||||
self.processor.target_column
|
||||
)
|
||||
bias_results = self.bias_analyzer.analyze()
|
||||
|
||||
# Step 4: Assess risks
|
||||
self.risk_analyzer = RiskAnalyzer(
|
||||
self.processor.df,
|
||||
self.trainer.results,
|
||||
bias_results,
|
||||
self.processor.protected_attributes,
|
||||
self.processor.target_column
|
||||
)
|
||||
risk_results = self.risk_analyzer.analyze()
|
||||
|
||||
# Step 5: Generate report
|
||||
self.report_generator = ReportGenerator(
|
||||
self.trainer.results,
|
||||
bias_results,
|
||||
risk_results,
|
||||
self.processor.df
|
||||
)
|
||||
|
||||
return self.report_generator.generate_report()
|
||||
|
||||
def save_report(self, report, output_path):
|
||||
"""
|
||||
Save report to JSON file
|
||||
|
||||
Args:
|
||||
report (dict): Analysis report
|
||||
output_path (str): Path to save JSON file
|
||||
|
||||
Returns:
|
||||
str: Path to saved file
|
||||
"""
|
||||
with open(output_path, 'w') as f:
|
||||
json.dump(report, f, indent=2, cls=NumpyEncoder)
|
||||
return output_path
|
||||
|
||||
def get_summary(self, report):
|
||||
"""
|
||||
Get executive summary from report
|
||||
|
||||
Args:
|
||||
report (dict): Analysis report
|
||||
|
||||
Returns:
|
||||
dict: Summary metrics
|
||||
"""
|
||||
return report.get('summary', {})
|
||||
263
src/report_generator.py
Normal file
263
src/report_generator.py
Normal file
@@ -0,0 +1,263 @@
|
||||
"""
|
||||
Report Generator Module
|
||||
Generates comprehensive JSON reports
|
||||
"""
|
||||
|
||||
import json
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
|
||||
class NumpyEncoder(json.JSONEncoder):
|
||||
"""Custom JSON encoder for numpy types"""
|
||||
def default(self, obj):
|
||||
if isinstance(obj, (np.integer, np.int64, np.int32)):
|
||||
return int(obj)
|
||||
elif isinstance(obj, (np.floating, np.float64, np.float32)):
|
||||
return float(obj)
|
||||
elif isinstance(obj, (np.ndarray,)):
|
||||
return obj.tolist()
|
||||
elif isinstance(obj, (np.bool_,)):
|
||||
return bool(obj)
|
||||
return super(NumpyEncoder, self).default(obj)
|
||||
|
||||
class ReportGenerator:
|
||||
"""Generate comprehensive analysis reports"""
|
||||
|
||||
def __init__(self, model_results, bias_results, risk_results, df):
|
||||
self.model_results = model_results
|
||||
self.bias_results = bias_results
|
||||
self.risk_results = risk_results
|
||||
self.df = df
|
||||
|
||||
def generate_report(self):
|
||||
"""Generate comprehensive JSON report"""
|
||||
report = {
|
||||
'metadata': self._generate_metadata(),
|
||||
'summary': self._generate_summary(),
|
||||
'model_performance': self._format_model_results(),
|
||||
'bias_analysis': self._format_bias_results(),
|
||||
'risk_assessment': self._format_risk_results(),
|
||||
'key_findings': self._extract_key_findings(),
|
||||
'recommendations': self._compile_recommendations(),
|
||||
'detailed_metrics': self._compile_detailed_metrics()
|
||||
}
|
||||
|
||||
return report
|
||||
|
||||
def _generate_metadata(self):
|
||||
"""Generate report metadata"""
|
||||
return {
|
||||
'report_id': f"AIGov_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
|
||||
'generated_at': datetime.now().isoformat(),
|
||||
'report_version': '1.0',
|
||||
'dataset_info': {
|
||||
'total_records': len(self.df),
|
||||
'total_features': len(self.df.columns),
|
||||
'columns': list(self.df.columns)
|
||||
}
|
||||
}
|
||||
|
||||
def _generate_summary(self):
|
||||
"""Generate executive summary"""
|
||||
model_metrics = self.model_results.get('metrics', {})
|
||||
|
||||
return {
|
||||
'overall_bias_score': self.bias_results.get('overall_bias_score', 0.0),
|
||||
'overall_risk_score': self.risk_results.get('overall_risk_score', 0.0),
|
||||
'risk_level': self.risk_results.get('risk_level', 'UNKNOWN'),
|
||||
'model_accuracy': model_metrics.get('accuracy', 0.0),
|
||||
'fairness_violations_count': len(self.bias_results.get('fairness_violations', [])),
|
||||
'passes_fairness_threshold': self.bias_results.get('fairness_assessment', {}).get('passes_fairness_threshold', False)
|
||||
}
|
||||
|
||||
def _format_model_results(self):
|
||||
"""Format model performance results"""
|
||||
return {
|
||||
'model_type': self.model_results.get('model_type', 'Unknown'),
|
||||
'metrics': self.model_results.get('metrics', {}),
|
||||
'confusion_matrix': self.model_results.get('confusion_matrix', []),
|
||||
'top_features': dict(list(self.model_results.get('feature_importance', {}).items())[:10])
|
||||
}
|
||||
|
||||
def _format_bias_results(self):
|
||||
"""Format bias analysis results"""
|
||||
return {
|
||||
'overall_bias_score': self.bias_results.get('overall_bias_score', 0.0),
|
||||
'fairness_metrics': self.bias_results.get('fairness_metrics', {}),
|
||||
'fairness_violations': self.bias_results.get('fairness_violations', []),
|
||||
'fairness_assessment': self.bias_results.get('fairness_assessment', {}),
|
||||
'demographic_bias_summary': self._summarize_demographic_bias()
|
||||
}
|
||||
|
||||
def _format_risk_results(self):
|
||||
"""Format risk assessment results"""
|
||||
return {
|
||||
'overall_risk_score': self.risk_results.get('overall_risk_score', 0.0),
|
||||
'risk_level': self.risk_results.get('risk_level', 'UNKNOWN'),
|
||||
'risk_categories': self.risk_results.get('risk_categories', {}),
|
||||
'privacy_risks': self._summarize_privacy_risks(),
|
||||
'ethical_risks': self._summarize_ethical_risks()
|
||||
}
|
||||
|
||||
def _summarize_demographic_bias(self):
|
||||
"""Summarize demographic bias"""
|
||||
demo_bias = self.bias_results.get('demographic_bias', {})
|
||||
summary = {}
|
||||
|
||||
for attr, data in demo_bias.items():
|
||||
summary[attr] = {
|
||||
'max_disparity': data.get('max_disparity', 0),
|
||||
'groups_analyzed': len(data.get('approval_rates', {}))
|
||||
}
|
||||
|
||||
return summary
|
||||
|
||||
def _summarize_privacy_risks(self):
|
||||
"""Summarize privacy risks"""
|
||||
privacy = self.risk_results.get('privacy_risks', {})
|
||||
|
||||
return {
|
||||
'pii_count': len(privacy.get('pii_detected', [])),
|
||||
'anonymization_level': privacy.get('anonymization_level', 'UNKNOWN'),
|
||||
'exposure_risk_count': len(privacy.get('exposure_risks', [])),
|
||||
'gdpr_compliance_score': privacy.get('gdpr_compliance', {}).get('compliance_score', 0)
|
||||
}
|
||||
|
||||
def _summarize_ethical_risks(self):
|
||||
"""Summarize ethical risks"""
|
||||
ethical = self.risk_results.get('ethical_risks', {})
|
||||
|
||||
return {
|
||||
'fairness_issues_count': len(ethical.get('fairness_issues', [])),
|
||||
'transparency_score': ethical.get('transparency_score', 0),
|
||||
'bias_amplification_risk': ethical.get('bias_amplification_risk', 'UNKNOWN'),
|
||||
'social_impact': ethical.get('social_impact_assessment', {})
|
||||
}
|
||||
|
||||
def _extract_key_findings(self):
|
||||
"""Extract key findings from analysis"""
|
||||
findings = []
|
||||
|
||||
# Model performance findings
|
||||
accuracy = self.model_results.get('metrics', {}).get('accuracy', 0)
|
||||
if accuracy >= 0.8:
|
||||
findings.append(f"✓ Model achieves good accuracy ({accuracy:.2%})")
|
||||
else:
|
||||
findings.append(f"⚠ Model accuracy is below optimal ({accuracy:.2%})")
|
||||
|
||||
# Bias findings
|
||||
bias_score = self.bias_results.get('overall_bias_score', 0)
|
||||
if bias_score < 0.3:
|
||||
findings.append("✓ Low bias detected across protected attributes")
|
||||
elif bias_score < 0.5:
|
||||
findings.append("⚠ Moderate bias detected - monitoring recommended")
|
||||
else:
|
||||
findings.append("❌ High bias detected - immediate action required")
|
||||
|
||||
# Fairness violations
|
||||
violations = self.bias_results.get('fairness_violations', [])
|
||||
if violations:
|
||||
high_sev = sum(1 for v in violations if v['severity'] == 'HIGH')
|
||||
findings.append(f"❌ {len(violations)} fairness violations detected ({high_sev} high severity)")
|
||||
else:
|
||||
findings.append("✓ No fairness violations detected")
|
||||
|
||||
# Privacy findings
|
||||
privacy = self.risk_results.get('privacy_risks', {})
|
||||
pii_count = len(privacy.get('pii_detected', []))
|
||||
if pii_count > 0:
|
||||
findings.append(f"⚠ {pii_count} columns contain potential PII")
|
||||
else:
|
||||
findings.append("✓ No obvious PII detected in dataset")
|
||||
|
||||
# Risk level
|
||||
risk_level = self.risk_results.get('risk_level', 'UNKNOWN')
|
||||
findings.append(f"Overall Risk Level: {risk_level}")
|
||||
|
||||
return findings
|
||||
|
||||
def _compile_recommendations(self):
|
||||
"""Compile all recommendations"""
|
||||
recommendations = []
|
||||
|
||||
# Get recommendations from each component
|
||||
privacy_recs = self.risk_results.get('privacy_risks', {}).get('recommendations', [])
|
||||
ethical_recs = self.risk_results.get('ethical_risks', {}).get('recommendations', [])
|
||||
performance_recs = self.risk_results.get('model_performance_risks', {}).get('recommendations', [])
|
||||
compliance_recs = self.risk_results.get('compliance_risks', {}).get('recommendations', [])
|
||||
|
||||
# Prioritize recommendations
|
||||
all_recs = []
|
||||
|
||||
# High priority (from violations and high risks)
|
||||
violations = self.bias_results.get('fairness_violations', [])
|
||||
if violations:
|
||||
all_recs.append({
|
||||
'priority': 'HIGH',
|
||||
'category': 'Fairness',
|
||||
'recommendation': 'Address fairness violations in protected attributes'
|
||||
})
|
||||
|
||||
if len(privacy_recs) > 0:
|
||||
all_recs.append({
|
||||
'priority': 'HIGH',
|
||||
'category': 'Privacy',
|
||||
'recommendation': privacy_recs[0]
|
||||
})
|
||||
|
||||
# Medium priority
|
||||
for rec in ethical_recs[:2]:
|
||||
all_recs.append({
|
||||
'priority': 'MEDIUM',
|
||||
'category': 'Ethics',
|
||||
'recommendation': rec
|
||||
})
|
||||
|
||||
# Lower priority
|
||||
for rec in performance_recs[:2]:
|
||||
all_recs.append({
|
||||
'priority': 'MEDIUM',
|
||||
'category': 'Performance',
|
||||
'recommendation': rec
|
||||
})
|
||||
|
||||
for rec in compliance_recs[:2]:
|
||||
all_recs.append({
|
||||
'priority': 'MEDIUM',
|
||||
'category': 'Compliance',
|
||||
'recommendation': rec
|
||||
})
|
||||
|
||||
# Convert to simple list with formatting
|
||||
recommendations = [
|
||||
f"[{r['priority']}] {r['category']}: {r['recommendation']}"
|
||||
for r in all_recs[:10] # Limit to top 10
|
||||
]
|
||||
|
||||
return recommendations
|
||||
|
||||
def _compile_detailed_metrics(self):
|
||||
"""Compile detailed metrics for analysis"""
|
||||
return {
|
||||
'bias_metrics': {
|
||||
'by_attribute': self.bias_results.get('fairness_metrics', {}),
|
||||
'demographic_analysis': self.bias_results.get('demographic_bias', {})
|
||||
},
|
||||
'risk_breakdown': {
|
||||
'privacy': self.risk_results.get('privacy_risks', {}),
|
||||
'ethical': self.risk_results.get('ethical_risks', {}),
|
||||
'compliance': self.risk_results.get('compliance_risks', {}),
|
||||
'data_quality': self.risk_results.get('data_quality_risks', {})
|
||||
},
|
||||
'model_details': {
|
||||
'classification_report': self.model_results.get('classification_report', {}),
|
||||
'feature_importance': self.model_results.get('feature_importance', {})
|
||||
}
|
||||
}
|
||||
|
||||
def save_report(self, filepath):
|
||||
"""Save report to JSON file"""
|
||||
report = self.generate_report()
|
||||
with open(filepath, 'w') as f:
|
||||
json.dump(report, f, indent=2, cls=NumpyEncoder)
|
||||
return filepath
|
||||
Reference in New Issue
Block a user