Implementing Cross-Industry Pattern Recognition: Code Examples and Production Systems

import asyncio
import numpy as np
import pandas as pd
from typing import Dict, List, Any, Tuple, Optional
from dataclasses import dataclass, field
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.decomposition import LatentDirichletAllocation
from sentence_transformers import SentenceTransformer
import networkx as nx
from datetime import datetime
import logging

@dataclass
class PatternMatch:
    source_pattern_id: str
    target_industry: str
    similarity_score: float
    adaptation_complexity: float
    market_fit_score: float
    implementation_risk: str
    expected_impact: Dict[str, float]
    confidence_interval: Tuple[float, float]

class AdvancedPatternRecognitionEngine:
    """Production-ready cross-industry pattern recognition engine."""

    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.logger = logging.getLogger(__name__)

        # Initialize ML models
        self.sentence_transformer = SentenceTransformer('all-MiniLM-L6-v2')
        self.tfidf_vectorizer = TfidfVectorizer(
            max_features=5000,
            stop_words='english',
            ngram_range=(1, 3)
        )
        self.lda_model = LatentDirichletAllocation(
            n_components=50,
            random_state=42,
            max_iter=100
        )

        # Pattern storage and indexing
        self.pattern_embeddings = {}
        self.pattern_graph = nx.Graph()
        self.industry_taxonomies = {}

        # Caching for performance
        self.similarity_cache = {}
        self.adaptation_cache = {}

    async def process_industry_data_batch(self, industry_data: List[Dict[str, Any]]) -> List[str]:
        """Process batch of industry data and extract patterns."""

        pattern_ids = []

        for industry_info in industry_data:
            try:
                # Extract multiple pattern types
                patterns = await self._extract_comprehensive_patterns(industry_info)

                for pattern in patterns:
                    pattern_id = await self._store_pattern(pattern)
                    pattern_ids.append(pattern_id)

                    # Update embeddings and graph
                    await self._update_pattern_embeddings(pattern)
                    await self._update_pattern_graph(pattern)

                self.logger.info(f"Processed {len(patterns)} patterns for industry {industry_info.get('name', 'unknown')}")

            except Exception as e:
                self.logger.error(f"Error processing industry data: {str(e)}")
                continue

        return pattern_ids

    async def _extract_comprehensive_patterns(self, industry_data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Extract multiple types of patterns from industry data."""

        patterns = []

        # Business model patterns
        business_patterns = await self._extract_business_model_patterns(industry_data)
        patterns.extend(business_patterns)

        # Technology adoption patterns
        tech_patterns = await self._extract_technology_patterns(industry_data)
        patterns.extend(tech_patterns)

        # Customer experience patterns
        cx_patterns = await self._extract_customer_experience_patterns(industry_data)
        patterns.extend(cx_patterns)

        # Operational efficiency patterns
        ops_patterns = await self._extract_operational_patterns(industry_data)
        patterns.extend(ops_patterns)

        # Value chain patterns
        value_chain_patterns = await self._extract_value_chain_patterns(industry_data)
        patterns.extend(value_chain_patterns)

        return patterns

    async def _extract_business_model_patterns(self, industry_data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Extract business model innovation patterns."""

        patterns = []

        companies = industry_data.get('companies', [])
        for company in companies:
            if not company.get('innovative_business_model'):
                continue

            # Analyze business model components
            revenue_model = self._analyze_revenue_model(company)
            value_proposition = self._analyze_value_proposition(company)
            customer_segments = self._analyze_customer_segments(company)
            cost_structure = self._analyze_cost_structure(company)

            pattern = {
                'pattern_type': 'business_model',
                'source_industry': industry_data['name'],
                'source_company': company['name'],
                'pattern_description': self._generate_business_model_description(
                    revenue_model, value_proposition, customer_segments, cost_structure
                ),
                'core_mechanisms': [
                    revenue_model['primary_mechanism'],
                    value_proposition['key_differentiator'],
                    customer_segments['targeting_strategy']
                ],
                'success_metrics': company.get('performance_metrics', {}),
                'implementation_requirements': {
                    'technology_stack': company.get('technology_requirements', []),
                    'organizational_capabilities': company.get('required_capabilities', []),
                    'market_conditions': company.get('success_conditions', [])
                },
                'adaptability_factors': self._assess_business_model_adaptability(
                    revenue_model, value_proposition, customer_segments, cost_structure
                ),
                'evidence_strength': self._calculate_evidence_strength(company),
                'pattern_maturity': self._assess_pattern_maturity(company)
            }

            patterns.append(pattern)

        return patterns

    async def find_cross_industry_opportunities(
        self,
        target_industry: str,
        business_objectives: List[str],
        constraints: Dict[str, Any] = None
    ) -> List[PatternMatch]:
        """Find cross-industry adaptation opportunities for target industry."""

        constraints = constraints or {}

        # Get target industry characteristics
        target_characteristics = await self._analyze_target_industry(target_industry)

        # Find relevant patterns from other industries
        candidate_patterns = await self._identify_candidate_patterns(
            target_industry, business_objectives, target_characteristics
        )

        # Score and rank opportunities
        pattern_matches = []

        for pattern in candidate_patterns:
            try:
                match_score = await self._calculate_comprehensive_match_score(
                    pattern, target_industry, target_characteristics, business_objectives
                )

                if match_score['overall_score'] > constraints.get('min_score', 0.6):
                    pattern_match = PatternMatch(
                        source_pattern_id=pattern['pattern_id'],
                        target_industry=target_industry,
                        similarity_score=match_score['similarity_score'],
                        adaptation_complexity=match_score['adaptation_complexity'],
                        market_fit_score=match_score['market_fit_score'],
                        implementation_risk=match_score['risk_level'],
                        expected_impact=match_score['expected_impact'],
                        confidence_interval=match_score['confidence_interval']
                    )
                    pattern_matches.append(pattern_match)

            except Exception as e:
                self.logger.error(f"Error scoring pattern {pattern.get('pattern_id', 'unknown')}: {str(e)}")
                continue

        # Sort by overall viability
        pattern_matches.sort(
            key=lambda x: (x.similarity_score + x.market_fit_score) / (1 + x.adaptation_complexity),
            reverse=True
        )

        return pattern_matches[:constraints.get('max_results', 20)]

    async def _calculate_comprehensive_match_score(
        self,
        pattern: Dict[str, Any],
        target_industry: str,
        target_characteristics: Dict[str, Any],
        objectives: List[str]
    ) -> Dict[str, Any]:
        """Calculate comprehensive matching score using multiple algorithms."""

        # Semantic similarity using sentence transformers
        semantic_similarity = await self._calculate_semantic_similarity(
            pattern, target_characteristics
        )

        # Structural similarity based on business model components
        structural_similarity = await self._calculate_structural_similarity(
            pattern, target_characteristics
        )

        # Market readiness assessment
        market_readiness = await self._assess_market_readiness(
            pattern, target_industry, target_characteristics
        )

        # Adaptation complexity analysis
        adaptation_complexity = await self._assess_adaptation_complexity(
            pattern, target_characteristics
        )

        # Strategic alignment with objectives
        strategic_alignment = self._assess_strategic_alignment(pattern, objectives)

        # Risk assessment
        implementation_risk = await self._assess_implementation_risk(
            pattern, target_industry
        )

        # Expected impact calculation
        expected_impact = await self._calculate_expected_impact(
            pattern, target_industry, target_characteristics
        )

        # Confidence interval calculation
        confidence_interval = self._calculate_confidence_interval([
            semantic_similarity, structural_similarity, market_readiness, strategic_alignment
        ])

        # Overall score calculation
        overall_score = (
            semantic_similarity * 0.25 +
            structural_similarity * 0.25 +
            market_readiness * 0.2 +
            strategic_alignment * 0.15 +
            (1 - adaptation_complexity) * 0.15  # Lower complexity = higher score
        )

        return {
            'overall_score': overall_score,
            'similarity_score': (semantic_similarity + structural_similarity) / 2,
            'market_fit_score': market_readiness,
            'adaptation_complexity': adaptation_complexity,
            'strategic_alignment': strategic_alignment,
            'risk_level': implementation_risk,
            'expected_impact': expected_impact,
            'confidence_interval': confidence_interval
        }

    async def _calculate_semantic_similarity(
        self, pattern: Dict[str, Any], target_characteristics: Dict[str, Any]
    ) -> float:
        """Calculate semantic similarity using sentence transformers."""

        # Create text representations
        pattern_text = self._create_pattern_text_representation(pattern)
        target_text = self._create_target_text_representation(target_characteristics)

        # Generate embeddings
        pattern_embedding = self.sentence_transformer.encode([pattern_text])
        target_embedding = self.sentence_transformer.encode([target_text])

        # Calculate cosine similarity
        similarity = cosine_similarity(pattern_embedding, target_embedding)[0][0]

        return float(similarity)

    async def _assess_adaptation_complexity(
        self, pattern: Dict[str, Any], target_characteristics: Dict[str, Any]
    ) -> float:
        """Assess complexity of adapting pattern to target industry."""

        complexity_factors = {}

        # Technology gap analysis
        pattern_tech = set(pattern.get('implementation_requirements', {}).get('technology_stack', []))
        target_tech = set(target_characteristics.get('existing_technologies', []))

        tech_gap = len(pattern_tech - target_tech) / len(pattern_tech) if pattern_tech else 0
        complexity_factors['technology_gap'] = tech_gap

        # Regulatory complexity
        pattern_regulation = pattern.get('regulatory_requirements', [])
        target_regulation = target_characteristics.get('regulatory_environment', {})

        regulatory_complexity = self._assess_regulatory_complexity(pattern_regulation, target_regulation)
        complexity_factors['regulatory_complexity'] = regulatory_complexity

        # Customer behavior gap
        pattern_customer = pattern.get('customer_characteristics', {})
        target_customer = target_characteristics.get('customer_profile', {})

        customer_gap = self._calculate_customer_behavior_gap(pattern_customer, target_customer)
        complexity_factors['customer_gap'] = customer_gap

        # Infrastructure requirements
        pattern_infrastructure = pattern.get('infrastructure_requirements', [])
        target_infrastructure = target_characteristics.get('existing_infrastructure', [])

        infrastructure_gap = self._calculate_infrastructure_gap(pattern_infrastructure, target_infrastructure)
        complexity_factors['infrastructure_gap'] = infrastructure_gap

        # Weighted complexity score
        complexity_score = (
            tech_gap * 0.3 +
            regulatory_complexity * 0.25 +
            customer_gap * 0.25 +
            infrastructure_gap * 0.2
        )

        return min(1.0, complexity_score)

    async def generate_adaptation_strategy(
        self, pattern_match: PatternMatch, implementation_constraints: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate detailed adaptation strategy for pattern implementation."""

        pattern = await self._get_pattern_by_id(pattern_match.source_pattern_id)

        # Phase-based implementation plan
        implementation_phases = await self._design_implementation_phases(
            pattern, pattern_match.target_industry, implementation_constraints
        )

        # Risk mitigation strategies
        risk_mitigations = await self._design_risk_mitigations(
            pattern, pattern_match.implementation_risk
        )

        # Resource requirements
        resource_requirements = await self._calculate_resource_requirements(
            pattern, pattern_match.target_industry, implementation_phases
        )

        # Success criteria and KPIs
        success_criteria = self._define_success_criteria(pattern, pattern_match)

        # Pilot program design
        pilot_program = await self._design_pilot_program(
            pattern, pattern_match.target_industry
        )

        return {
            'adaptation_strategy_id': f"adapt_{pattern_match.source_pattern_id}_{pattern_match.target_industry}",
            'implementation_phases': implementation_phases,
            'risk_mitigations': risk_mitigations,
            'resource_requirements': resource_requirements,
            'success_criteria': success_criteria,
            'pilot_program': pilot_program,
            'estimated_timeline': self._calculate_implementation_timeline(implementation_phases),
            'expected_outcomes': pattern_match.expected_impact,
            'monitoring_framework': self._design_monitoring_framework(success_criteria)
        }

import asyncio
from kafka import KafkaConsumer, KafkaProducer
import redis
import json
from datetime import datetime, timedelta

class RealTimePatternDiscovery:
    """Real-time system for discovering new patterns from streaming industry data."""

    def __init__(self, kafka_config: Dict, redis_config: Dict, pattern_engine):
        self.kafka_consumer = KafkaConsumer(
            'industry-data-stream',
            bootstrap_servers=kafka_config['bootstrap_servers'],
            value_deserializer=lambda x: json.loads(x.decode('utf-8'))
        )

        self.kafka_producer = KafkaProducer(
            bootstrap_servers=kafka_config['bootstrap_servers'],
            value_serializer=lambda x: json.dumps(x).encode('utf-8')
        )

        self.redis_client = redis.Redis(**redis_config)
        self.pattern_engine = pattern_engine
        self.pattern_buffer = {}

    async def start_real_time_discovery(self):
        """Start real-time pattern discovery from streaming data."""

        self.logger.info("Starting real-time pattern discovery")

        tasks = [
            asyncio.create_task(self._process_data_stream()),
            asyncio.create_task(self._analyze_pattern_buffer()),
            asyncio.create_task(self._update_opportunity_alerts()),
            asyncio.create_task(self._maintain_pattern_freshness())
        ]

        await asyncio.gather(*tasks)

    async def _process_data_stream(self):
        """Process incoming industry data stream."""

        for message in self.kafka_consumer:
            try:
                industry_update = message.value

                # Extract patterns from update
                patterns = await self.pattern_engine.extract_patterns_from_update(industry_update)

                # Buffer patterns for batch processing
                industry_id = industry_update.get('industry_id')
                if industry_id not in self.pattern_buffer:
                    self.pattern_buffer[industry_id] = []

                self.pattern_buffer[industry_id].extend(patterns)

                # Check for immediate high-value patterns
                for pattern in patterns:
                    if pattern.get('priority_score', 0) > 0.9:
                        await self._process_high_priority_pattern(pattern)

            except Exception as e:
                self.logger.error(f"Error processing data stream: {str(e)}")
                continue

    async def _analyze_pattern_buffer(self):
        """Analyze buffered patterns for cross-industry opportunities."""

        while True:
            try:
                if self.pattern_buffer:
                    # Process each industry's patterns
                    for industry_id, patterns in self.pattern_buffer.items():
                        if len(patterns) >= 5:  # Minimum batch size
                            await self._process_pattern_batch(industry_id, patterns)
                            self.pattern_buffer[industry_id] = []

                await asyncio.sleep(60)  # Process every minute

            except Exception as e:
                self.logger.error(f"Error analyzing pattern buffer: {str(e)}")
                await asyncio.sleep(30)