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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
}
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