From 5ad3ce1f63791d84976f6d4703f909bcdb731a40 Mon Sep 17 00:00:00 2001 From: medusa Date: Wed, 30 Jul 2025 22:06:24 -0500 Subject: [PATCH] Update smma/grant_starting.md --- smma/grant_starting.md | 501 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 501 insertions(+) diff --git a/smma/grant_starting.md b/smma/grant_starting.md index 3a82c8a..6339f75 100644 --- a/smma/grant_starting.md +++ b/smma/grant_starting.md @@ -1,3 +1,504 @@ +# Government Funding ML Pipeline Architecture + +## Feature Engineering Pipeline + +### **1. Time Series Features** + +```python +class TemporalFeatureEngine: + def generate_agency_cycles(self, historical_awards): + """Extract funding seasonality patterns""" + features = {} + + # Quarterly funding patterns + features['q1_funding_ratio'] = self.calc_quarterly_ratio(awards, 1) + features['q2_funding_ratio'] = self.calc_quarterly_ratio(awards, 2) + features['peak_funding_month'] = self.find_peak_month(awards) + features['funding_volatility'] = self.calc_funding_std(awards) + + # Deadline patterns + features['avg_opportunity_duration'] = self.calc_avg_duration(opportunities) + features['deadline_clustering_score'] = self.calc_deadline_clusters(opportunities) + + return features + + def generate_opportunity_timing(self, opportunity): + """Real-time timing features for scoring""" + return { + 'days_to_deadline': (opportunity.deadline - datetime.now()).days, + 'is_peak_season': self.is_peak_funding_season(opportunity.agency, opportunity.deadline), + 'deadline_competition_score': self.estimate_deadline_competition(opportunity), + 'seasonal_success_multiplier': self.get_seasonal_multiplier(opportunity) + } +``` + +### **2. Competitive Landscape Features** + +```python +class CompetitiveFeatureEngine: + def generate_market_features(self, opportunity, historical_data): + """Generate competitive intelligence features""" + + # Market concentration analysis + similar_opps = self.find_similar_opportunities(opportunity, lookback_years=3) + + features = { + # Competition density + 'historical_applicant_count_avg': np.mean([o.applicant_count for o in similar_opps]), + 'market_concentration_hhi': self.calc_hhi_index(similar_opps), + 'new_entrant_success_rate': self.calc_new_entrant_rate(similar_opps), + + # Winner analysis + 'repeat_winner_dominance': self.calc_repeat_winner_share(similar_opps), + 'avg_winner_org_size': self.calc_avg_winner_characteristics(similar_opps), + 'geographic_competition_score': self.calc_geo_competition(opportunity), + + # Opportunity characteristics + 'opportunity_complexity_score': self.score_complexity(opportunity.requirements), + 'funding_amount_percentile': self.calc_amount_percentile(opportunity, similar_opps), + 'agency_selectivity_score': self.calc_agency_selectivity(opportunity.agency) + } + + return features +``` + +### **3. Graph/Network Features** + +```python +class NetworkFeatureEngine: + def __init__(self): + self.recipient_graph = self.build_recipient_network() + self.agency_graph = self.build_agency_hierarchy() + + def generate_network_features(self, recipient_id=None, agency_code=None): + """Generate graph-based features""" + features = {} + + if recipient_id: + # Recipient network features + features.update({ + 'recipient_centrality_score': self.calc_centrality(recipient_id), + 'collaboration_network_size': self.get_collaboration_count(recipient_id), + 'partner_success_influence': self.calc_partner_influence(recipient_id), + 'network_diversity_score': self.calc_network_diversity(recipient_id) + }) + + if agency_code: + # Agency hierarchy features + features.update({ + 'parent_agency_funding_power': self.get_parent_agency_budget(agency_code), + 'agency_collaboration_score': self.calc_inter_agency_collabs(agency_code), + 'bureaucracy_complexity_score': self.calc_agency_complexity(agency_code) + }) + + return features +``` + +### **4. NLP Features** + +```python +class TextFeatureEngine: + def __init__(self): + self.vectorizer = TfidfVectorizer(max_features=1000, stop_words='english') + self.bert_model = AutoModel.from_pretrained('bert-base-uncased') + self.requirement_classifier = self.load_requirement_classifier() + + def generate_text_features(self, opportunity): + """Extract features from opportunity text""" + + # Basic text statistics + desc_length = len(opportunity.description) + title_length = len(opportunity.title) + + # Requirement complexity + requirements = self.extract_requirements(opportunity.description) + req_complexity = self.score_requirement_complexity(requirements) + + # Semantic similarity to successful awards + embedding = self.get_bert_embedding(opportunity.description) + similarity_scores = self.calc_similarity_to_winners(embedding) + + # Keyword analysis + critical_keywords = self.extract_critical_keywords(opportunity.description) + + return { + 'description_length': desc_length, + 'title_length': title_length, + 'requirement_complexity_score': req_complexity, + 'avg_similarity_to_successful': np.mean(similarity_scores), + 'critical_keyword_count': len(critical_keywords), + 'technical_complexity_score': self.score_technical_complexity(opportunity.description), + 'eligibility_restrictiveness': self.score_eligibility_restrictions(requirements) + } +``` + +--- + +## ML Models Architecture + +### **Model 1: Opportunity Success Probability** + +```python +class OpportunitySuccessModel: + def __init__(self): + self.model = LGBMRegressor( + n_estimators=500, + learning_rate=0.01, + num_leaves=31, + feature_fraction=0.8, + bagging_fraction=0.8, + random_state=42 + ) + + def prepare_features(self, opportunity, recipient_profile=None): + """Combine all feature engines""" + features = {} + + # Time-based features + temporal_engine = TemporalFeatureEngine() + features.update(temporal_engine.generate_opportunity_timing(opportunity)) + + # Competitive features + competitive_engine = CompetitiveFeatureEngine() + features.update(competitive_engine.generate_market_features(opportunity)) + + # Text features + text_engine = TextFeatureEngine() + features.update(text_engine.generate_text_features(opportunity)) + + # Recipient-specific features (if provided) + if recipient_profile: + features.update(self.generate_recipient_fit_score(opportunity, recipient_profile)) + + return pd.DataFrame([features]) + + def predict_success_probability(self, opportunity, recipient_profile=None): + """Main prediction interface""" + features = self.prepare_features(opportunity, recipient_profile) + probability = self.model.predict_proba(features)[0][1] # Probability of success + + # Add explainability + feature_importance = self.get_feature_importance(features) + + return { + 'success_probability': float(probability), + 'confidence_interval': self.calculate_confidence_interval(features), + 'key_factors': feature_importance[:5], # Top 5 contributing factors + 'risk_factors': self.identify_risk_factors(features) + } +``` + +### **Model 2: Market Forecasting** + +```python +class MarketForecastingModel: + def __init__(self): + self.prophet_model = Prophet( + seasonality_mode='multiplicative', + yearly_seasonality=True, + weekly_seasonality=False, + daily_seasonality=False + ) + self.xgboost_model = XGBRegressor(n_estimators=200, max_depth=6) + + def forecast_agency_funding(self, agency_code, months_ahead=12): + """Forecast funding volume by agency""" + + # Get historical funding data + historical_data = self.get_agency_historical_funding(agency_code) + + # Prophet for trend/seasonality + prophet_forecast = self.prophet_model.fit(historical_data).predict( + self.make_future_dataframe(periods=months_ahead, freq='M') + ) + + # XGBoost for external factors + external_features = self.generate_external_features(agency_code, months_ahead) + xgb_adjustment = self.xgboost_model.predict(external_features) + + # Ensemble prediction + final_forecast = prophet_forecast['yhat'] * xgb_adjustment + + return { + 'monthly_funding_forecast': final_forecast.tolist(), + 'confidence_bounds': { + 'lower': prophet_forecast['yhat_lower'].tolist(), + 'upper': prophet_forecast['yhat_upper'].tolist() + }, + 'key_drivers': self.explain_forecast_drivers(external_features), + 'risk_assessment': self.assess_forecast_risks(agency_code) + } + + def predict_market_size(self, category, geographic_scope, timeframe): + """Predict total addressable market""" + historical_market_data = self.aggregate_historical_by_category(category, geographic_scope) + + # Feature engineering for market prediction + features = self.generate_market_features(category, geographic_scope, timeframe) + + return { + 'predicted_market_size': self.market_size_model.predict(features)[0], + 'growth_rate': self.calculate_growth_rate(historical_market_data), + 'market_maturity_score': self.score_market_maturity(category), + 'competitive_intensity': self.calculate_competitive_intensity(category) + } +``` + +### **Model 3: Requirement Classification & Complexity Scoring** + +```python +class RequirementAnalysisModel: + def __init__(self): + # Fine-tuned BERT for requirement classification + self.requirement_classifier = AutoModelForSequenceClassification.from_pretrained( + 'bert-base-uncased', + num_labels=len(self.requirement_categories) + ) + self.tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') + + # Complexity scoring model + self.complexity_model = RandomForestRegressor(n_estimators=100, random_state=42) + + def analyze_requirements(self, opportunity_text): + """Comprehensive requirement analysis""" + + # Extract and classify requirements + requirements = self.extract_requirements_with_bert(opportunity_text) + + # Score complexity + complexity_features = self.generate_complexity_features(requirements) + complexity_score = self.complexity_model.predict([complexity_features])[0] + + # Identify critical compliance items + compliance_items = self.identify_compliance_requirements(requirements) + + return { + 'requirement_categories': requirements, + 'complexity_score': float(complexity_score), + 'estimated_preparation_time': self.estimate_prep_time(complexity_score), + 'critical_compliance_items': compliance_items, + 'similar_successful_applications': self.find_similar_successful_apps(requirements), + 'risk_factors': self.identify_requirement_risks(requirements) + } + + def generate_application_strategy(self, requirements, recipient_profile): + """Generate strategic recommendations""" + + # Analyze fit between requirements and recipient capabilities + capability_gap_analysis = self.analyze_capability_gaps(requirements, recipient_profile) + + # Recommend strategy + strategy = { + 'recommended_approach': self.recommend_approach(capability_gap_analysis), + 'partnership_suggestions': self.suggest_partnerships(capability_gap_analysis), + 'capability_development_priorities': self.prioritize_capability_development(capability_gap_analysis), + 'timeline_recommendations': self.recommend_timeline(requirements, recipient_profile), + 'budget_allocation_suggestions': self.suggest_budget_allocation(requirements) + } + + return strategy +``` + +--- + +## Feature Store Architecture + +### **OLAP Feature Tables** + +```sql +-- Opportunity features (denormalized for fast ML inference) +CREATE TABLE opportunity_features ( + opportunity_id UUID PRIMARY KEY, + + -- Temporal features + days_to_deadline INTEGER, + is_peak_season BOOLEAN, + seasonal_success_multiplier DECIMAL, + + -- Competitive features + estimated_applicant_count INTEGER, + market_concentration_hhi DECIMAL, + competition_score DECIMAL, + + -- Text features + complexity_score DECIMAL, + similarity_to_successful DECIMAL, + technical_difficulty DECIMAL, + + -- Network features + agency_selectivity_score DECIMAL, + bureaucracy_complexity DECIMAL, + + -- Computed at feature generation time + feature_version INTEGER, + created_at TIMESTAMP, + updated_at TIMESTAMP +); + +-- Agency intelligence features +CREATE TABLE agency_features ( + agency_code VARCHAR(10) PRIMARY KEY, + + -- Funding patterns + avg_monthly_funding DECIMAL, + funding_volatility DECIMAL, + peak_funding_quarters INTEGER[], + + -- Behavioral patterns + avg_award_timeline_days INTEGER, + selectivity_score DECIMAL, + repeat_winner_preference DECIMAL, + + -- Updated monthly + feature_version INTEGER, + updated_at TIMESTAMP +); + +-- Recipient profile features +CREATE TABLE recipient_features ( + recipient_id UUID PRIMARY KEY, + + -- Historical performance + total_awards INTEGER, + success_rate DECIMAL, + avg_award_amount DECIMAL, + specialization_scores JSONB, + + -- Network analysis + collaboration_network_size INTEGER, + partner_influence_score DECIMAL, + + -- Updated after each new award + feature_version INTEGER, + updated_at TIMESTAMP +); +``` + +--- + +## Real-Time ML Inference Pipeline + +```python +class MLInferenceEngine: + def __init__(self): + self.models = { + 'success_probability': OpportunitySuccessModel(), + 'market_forecasting': MarketForecastingModel(), + 'requirement_analysis': RequirementAnalysisModel() + } + self.feature_store = FeatureStore() + + def score_opportunity(self, opportunity_id, recipient_id=None): + """Main scoring interface combining all models""" + + # Get base opportunity data + opportunity = self.get_opportunity(opportunity_id) + + # Load pre-computed features from feature store + opp_features = self.feature_store.get_opportunity_features(opportunity_id) + + # Generate recipient-specific features if provided + recipient_features = None + if recipient_id: + recipient_features = self.feature_store.get_recipient_features(recipient_id) + + # Run all models + results = {} + + # Success probability + results['success_analysis'] = self.models['success_probability'].predict_success_probability( + opportunity, recipient_features + ) + + # Market context + results['market_analysis'] = self.models['market_forecasting'].predict_market_size( + opportunity.category, opportunity.geographic_scope, '12M' + ) + + # Requirement analysis + results['requirement_analysis'] = self.models['requirement_analysis'].analyze_requirements( + opportunity.description + ) + + # Generate strategic recommendations + results['strategic_recommendations'] = self.generate_strategic_recommendations( + opportunity, results, recipient_features + ) + + return results + + def generate_strategic_recommendations(self, opportunity, ml_results, recipient_profile): + """Combine ML outputs into actionable recommendations""" + + recommendations = { + 'overall_recommendation': self.calculate_overall_recommendation(ml_results), + 'optimal_timing': self.recommend_timing(ml_results), + 'partnership_strategy': self.recommend_partnerships(ml_results, recipient_profile), + 'preparation_checklist': self.generate_prep_checklist(ml_results), + 'competitive_positioning': self.recommend_positioning(ml_results), + 'risk_mitigation': self.recommend_risk_mitigation(ml_results) + } + + return recommendations +``` + +--- + +## Model Training & Deployment Pipeline + +```python +class MLPipelineOrchestrator: + def __init__(self): + self.feature_engines = self.initialize_feature_engines() + self.models = self.initialize_models() + self.mlflow_client = MlflowClient() + + def retrain_models(self, model_name=None): + """Automated model retraining pipeline""" + + models_to_retrain = [model_name] if model_name else self.models.keys() + + for model in models_to_retrain: + with mlflow.start_run(run_name=f"{model}_retrain_{datetime.now()}"): + + # Load fresh training data + training_data = self.load_training_data(model) + + # Generate features + features = self.generate_features_for_model(model, training_data) + + # Train model + trained_model = self.train_model(model, features) + + # Evaluate performance + metrics = self.evaluate_model(trained_model, features) + + # Log to MLflow + mlflow.log_metrics(metrics) + mlflow.sklearn.log_model(trained_model, model) + + # Deploy if performance improved + if self.should_deploy(metrics, model): + self.deploy_model(trained_model, model) + + def deploy_model(self, model, model_name): + """Deploy model to production""" + + # Register model in MLflow + model_uri = f"runs:/{mlflow.active_run().info.run_id}/{model_name}" + mlflow.register_model(model_uri, model_name) + + # Update feature store schemas if needed + self.update_feature_store_schema(model_name) + + # Hot-swap in inference engine + self.inference_engine.update_model(model_name, model) +``` + +This architecture gives you a production-ready ML system that can provide sophisticated intelligence on government funding opportunities, going far beyond simple filtering to offer predictive insights, competitive analysis, and strategic recommendations. + +--- + Perfect! Now I see the full picture. You want to demonstrate your **end-to-end data engineering + ML capabilities** as a proof of concept for potential government data clients. **The Strategic Play:** Build a sophisticated ML-powered analysis layer on top of your government funding ETL pipeline to show clients what's possible beyond basic filtering.