ml_engine/predictors.py

from typing import Dict, Any
import pandas as pd
import numpy as np
from .base_predictor import BasePredictor

class FintechPredictor(BasePredictor):
    """
    Predicts:
    1. Days until next funding
    2. Funding amount
    3. Round series
    """
    def _preprocess(self, data: Dict) -> pd.DataFrame:
        # Extract the 32 features defined in the prompt
        features = {
            'download_velocity_30d': data.get('download_velocity', 0),
            'hiring_spike': 1 if data.get('hiring_spike') == 'Active' else 0,
            'review_sentiment': data.get('review_sentiment', 0),
            # Add placeholders for other features to match model expectations
            'competitor_funding_gap': np.random.randint(0, 180), # Mock for now
            'burn_rate_proxy': np.random.uniform(0.5, 5.0)      # Mock for now
        }
        return pd.DataFrame([features])

    def _run_inference(self, features: pd.DataFrame) -> Dict[str, Any]:
        # Placeholder logic until real models are trained
        # In reality, self.models['days_to_funding'].predict(features)
        
        # Heuristic-based "prediction" for demo
        hiring_strength = features['hiring_spike'].iloc[0]
        downloads = features['download_velocity_30d'].iloc[0]
        
        days_to_funding = max(14, 120 - (downloads * 0.5) - (hiring_strength * 30))
        funding_amount = (downloads * 10000) + (hiring_strength * 5000000)
        
        return {
            'days_to_funding': int(days_to_funding),
            'funding_amount': round(funding_amount, -5), # Round to nearest 100k
            'round_series': 'Series B' if funding_amount > 20000000 else 'Series A'
        }

class AiTalentPredictor(BasePredictor):
    """
    Predicts:
    1. Next model release date
    2. Performance leap magnitude
    3. Commercialization timeline
    """
    def _preprocess(self, data: Dict) -> pd.DataFrame:
        features = {
            'github_stars_7d': data.get('github_stars_7d', 0),
            'arxiv_papers': data.get('arxiv_papers', 0),
            'talent_score': data.get('talent_score', 0)
        }
        return pd.DataFrame([features])

    def _run_inference(self, features: pd.DataFrame) -> Dict[str, Any]:
        stars = features['github_stars_7d'].iloc[0]
        papers = features['arxiv_papers'].iloc[0]
        
        days_to_release = max(30, 180 - (stars * 0.1) - (papers * 2))
        perf_leap = min(50, (stars * 0.05) + (papers * 1.5))
        
        return {
            'next_release_days': int(days_to_release),
            'performance_leap_pct': round(perf_leap, 1),
            'commercialization_months': int(days_to_release / 30) + 2
        }

class EsgPredictor(BasePredictor):
    """
    Predicts:
    1. Greenwashing exposure score
    2. Correction timing
    3. Fine probability
    """
    def _preprocess(self, data: Dict) -> pd.DataFrame:
        features = {
            'esg_claims': data.get('esg_claims', 0),
            'verifiable_actions': data.get('verifiable_actions', 0),
            'greenwashing_index': data.get('greenwashing_index', 0)
        }
        return pd.DataFrame([features])

    def _run_inference(self, features: pd.DataFrame) -> Dict[str, Any]:
        claims = features['esg_claims'].iloc[0]
        verified = features['verifiable_actions'].iloc[0]
        
        gap = max(0, claims - verified)
        risk_score = min(100, gap * 5)
        
        return {
            'greenwashing_score': int(risk_score),
            'correction_days': int(max(7, 90 - risk_score)),
            'fine_probability': 'High' if risk_score > 60 else 'Low'
        }

class RegulatoryPredictor(BasePredictor):
    def _preprocess(self, data: Dict) -> pd.DataFrame:
        return pd.DataFrame([data]) # Pass through for now

    def _run_inference(self, features: pd.DataFrame) -> Dict[str, Any]:
        return {
            'enforcement_probability': 0.75,
            'estimated_fine': 5000000,
            'action_timeline_days': 45
        }

class SupplyChainPredictor(BasePredictor):
    def _preprocess(self, data: Dict) -> pd.DataFrame:
        return pd.DataFrame([data])

    def _run_inference(self, features: pd.DataFrame) -> Dict[str, Any]:
        return {
            'disruption_risk_score': 65,
            'recovery_time_days': 14,
            'impact_revenue_pct': 3.5
        }