🧘‍♂️ How to Train an AI That Keeps You Financially Sane

def comprehensive_trigger_mapping(user_data, historical_patterns):
    """
    Advanced emotional trigger mapping with ML pattern recognition.
    """
    def analyze_stress_patterns(data):
        return {'work': data.get('work_stress', 0), 'social': data.get('social_stress', 0)}
    
    def identify_spending_correlations(patterns):
        return {'impulse': patterns.get('impulse_spend', 0), 'stress': patterns.get('stress_spend', 0)}
    
    def map_productivity_cycles(data):
        return {'focus': data.get('focus_hours', 0), 'deep_work': data.get('deep_work', 0)}
    
    def assess_social_influence_patterns(data):
        return {'peer_pressure': data.get('social_influence', 0)}
    
    def detect_seasonal_patterns(patterns):
        return {'seasonal_spikes': patterns.get('seasonal', 0)}
    
    def correlate_health_spending_patterns(data):
        return {'health_spend': data.get('health', 0)}
    
    def calculate_trigger_interactions(categories):
        return {k: sum(v.values()) / len(v) for k, v in categories.items()}
    
    def identify_compounding_effects(matrix):
        return [k for k, v in matrix.items() if v > 0.7]
    
    def create_intervention_strategy(trigger, data):
        return f'Mitigate {trigger}' if data.get(trigger, 0) > 0.5 else 'Monitor'
    
    def calculate_mapping_confidence(data):
        return max(0.7, 1 - sum(data.values()) * 0.1)
    
    def prioritize_interventions(strategies):
        return sorted(strategies.items(), key=lambda x: x[1], reverse=True)
    
    def generate_cat_wisdom_for_triggers(categories):
        return '😻 Purr through stress' if sum(categories.values()) < 2 else '😿 Cat therapy needed'
    
    trigger_categories = {
        'stress_triggers': analyze_stress_patterns(user_data),
        'spending_triggers': identify_spending_correlations(historical_patterns),
        'productivity_triggers': map_productivity_cycles(user_data),
        'social_triggers': assess_social_influence_patterns(user_data),
        'seasonal_triggers': detect_seasonal_patterns(historical_patterns),
        'health_triggers': correlate_health_spending_patterns(user_data)
    }
    
    interaction_matrix = calculate_trigger_interactions(trigger_categories)
    risk_amplifiers = identify_compounding_effects(interaction_matrix)
    intervention_strategies = {
        trigger: create_intervention_strategy(trigger, user_data) 
        for trigger in trigger_categories.keys()
    }
    
    return {
        'trigger_map': trigger_categories,
        'interaction_effects': interaction_matrix,
        'risk_amplifiers': risk_amplifiers,
        'interventions': intervention_strategies,
        'confidence_score': f'{calculate_mapping_confidence(user_data):.1%}',
        'next_actions': prioritize_interventions(intervention_strategies),
        'cat_insights': generate_cat_wisdom_for_triggers(interaction_matrix)
    }