Feat:Create the Basic Ai Governance Package to use has a guide

src/__init__.py

@@ -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', {})