"""Test module for enhanced feature imputation functionality."""

import pytest
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

# Test the enhanced functions that we'll add to feature_engineer.py
# For now, import existing functions and test their enhancement


class TestFeatureImputation:
    """Test cases for enhanced feature imputation."""
    
    @pytest.fixture
    def sample_data_with_gaps(self):
        """Create sample data with date gaps for testing imputation."""
        dates = []
        base_date = datetime(2024, 1, 1)
        
        # Create irregular date sequence with gaps
        for i in [0, 1, 3, 5, 8, 12, 15, 20, 25, 30]:  # Skip days 2, 4, 6, 7, etc.
            dates.append(base_date + timedelta(days=i))
        
        data = []
        for dataset_uuid in ['uuid1']:
            for rule_code in ['R001']:
                for i, date in enumerate(dates):
                    data.append({
                        'dataset_uuid': dataset_uuid,
                        'rule_code': rule_code,
                        'business_date': date,
                        'pass_percentage': 50.0 + (i * 5.0),  # Predictable values
                        'source': 'test_source',
                        'tenant_id': 'test_tenant',
                        'dataset_name': 'test_dataset'
                    })
        
        return pd.DataFrame(data)
    
    def test_find_closest_lag_value_function_exists(self):
        """Test that enhanced function find_closest_lag_value exists."""
        # This test should fail initially since the function doesn't exist
        from src.data_quality_summarizer.ml.feature_engineer import find_closest_lag_value
        
        # Create test data
        data = pd.DataFrame({
            'business_date': [datetime(2024, 1, 1), datetime(2024, 1, 3), datetime(2024, 1, 8)],
            'pass_percentage': [10.0, 20.0, 40.0]
        })
        
        # Test finding closest lag value
        current_date = datetime(2024, 1, 8)
        lag_value = find_closest_lag_value(data, current_date, lag_days=7, tolerance_days=3)
        
        # Should find Jan 1 value (closest within tolerance)
        assert lag_value == 10.0
    
    def test_get_imputation_strategy_function_exists(self):
        """Test that get_imputation_strategy function exists."""
        from src.data_quality_summarizer.ml.feature_engineer import get_imputation_strategy
        
        strategy = get_imputation_strategy()
        
        assert isinstance(strategy, dict)
        assert 'lag_features' in strategy
        assert 'default_value' in strategy['lag_features']
    
    def test_calculate_flexible_moving_average_function_exists(self):
        """Test that calculate_flexible_moving_average function exists."""
        from src.data_quality_summarizer.ml.feature_engineer import calculate_flexible_moving_average
        
        # Create test data
        data = pd.DataFrame({
            'business_date': pd.date_range('2024-01-01', periods=10),
            'pass_percentage': range(10, 20)
        })
        
        result = calculate_flexible_moving_average(data, window_size=7, min_periods=1)
        
        assert isinstance(result, pd.DataFrame)
        assert 'avg_7_day' in result.columns or len(result) > 0  # Should work with flexible params