mirror of
https://github.com/PlatypusPus/MushroomEmpire.git
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147 lines
4.4 KiB
Python
147 lines
4.4 KiB
Python
"""
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AI Governance Module - Bias Detection and Risk Analysis
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"""
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from .data_processor import DataProcessor
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from .model_trainer import GeneralizedModelTrainer
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from .bias_analyzer import BiasAnalyzer
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from .risk_analyzer import RiskAnalyzer
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from .report_generator import ReportGenerator, NumpyEncoder
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import pandas as pd
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import json
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__version__ = '1.0.0'
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__all__ = [
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'DataProcessor',
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'GeneralizedModelTrainer',
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'BiasAnalyzer',
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'RiskAnalyzer',
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'ReportGenerator',
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'NumpyEncoder',
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'AIGovernanceAnalyzer'
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]
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class AIGovernanceAnalyzer:
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"""
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Main interface for AI Governance analysis
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Example:
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>>> analyzer = AIGovernanceAnalyzer()
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>>> report = analyzer.analyze('data.csv', 'target', ['gender', 'age'])
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>>> print(f"Bias Score: {report['summary']['overall_bias_score']:.3f}")
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"""
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def __init__(self):
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"""Initialize the analyzer"""
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self.processor = None
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self.trainer = None
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self.bias_analyzer = None
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self.risk_analyzer = None
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self.report_generator = None
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def analyze(self, data_path, target_column, protected_attributes):
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"""
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Run complete AI governance analysis from file
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Args:
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data_path (str): Path to CSV file
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target_column (str): Name of target column
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protected_attributes (list): List of protected attribute column names
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Returns:
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dict: Complete analysis report
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"""
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df = pd.read_csv(data_path)
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return self.analyze_dataframe(df, target_column, protected_attributes)
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def analyze_dataframe(self, df, target_column, protected_attributes):
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"""
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Run complete AI governance analysis from DataFrame
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Args:
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df (pd.DataFrame): Input dataframe
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target_column (str): Name of target column
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protected_attributes (list): List of protected attribute column names
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Returns:
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dict: Complete analysis report
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"""
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# Step 1: Process data
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self.processor = DataProcessor(df)
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self.processor.target_column = target_column
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self.processor.protected_attributes = protected_attributes
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self.processor.prepare_data()
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# Step 2: Train model
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self.trainer = GeneralizedModelTrainer(
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self.processor.X_train,
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self.processor.X_test,
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self.processor.y_train,
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self.processor.y_test,
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self.processor.feature_names
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)
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self.trainer.train()
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self.trainer.evaluate()
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# Step 3: Analyze bias (Presidio disabled by default to avoid initialization issues)
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self.bias_analyzer = BiasAnalyzer(
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self.processor.X_test,
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self.processor.y_test,
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self.trainer.y_pred,
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self.processor.df,
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self.processor.protected_attributes,
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self.processor.target_column,
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use_presidio=False # Set to True to enable Presidio-enhanced detection
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)
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bias_results = self.bias_analyzer.analyze()
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# Step 4: Assess risks
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self.risk_analyzer = RiskAnalyzer(
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self.processor.df,
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self.trainer.results,
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bias_results,
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self.processor.protected_attributes,
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self.processor.target_column
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)
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risk_results = self.risk_analyzer.analyze()
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# Step 5: Generate report
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self.report_generator = ReportGenerator(
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self.trainer.results,
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bias_results,
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risk_results,
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self.processor.df
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)
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return self.report_generator.generate_report()
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def save_report(self, report, output_path):
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"""
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Save report to JSON file
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Args:
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report (dict): Analysis report
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output_path (str): Path to save JSON file
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Returns:
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str: Path to saved file
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"""
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with open(output_path, 'w') as f:
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json.dump(report, f, indent=2, cls=NumpyEncoder)
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return output_path
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def get_summary(self, report):
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"""
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Get executive summary from report
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Args:
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report (dict): Analysis report
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Returns:
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dict: Summary metrics
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"""
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return report.get('summary', {})
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