from fbprophet import Prophet
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
class EnhancedQuantumProphetPredictor:
def __init__(self, quantum_features, geopolitical_data):
self.quantum_features = quantum_features
self.geopolitical_data = geopolitical_data
self.prophet_model = Prophet()
self.quantum_model = make_pipeline(StandardScaler(), RandomForestRegressor())
def train_models(self, historical_data):
self.prophet_model.fit(historical_data[['ds', 'y']])
geopolitical_features = self._extract_geopolitical_features(historical_data)
combined_features = self._combine_features(geopolitical_features)
target_variable = historical_data['y']
self.quantum_model.fit(combined_features, target_variable)
def predict_future_events(self, future_data):
future_data_prepared = future_data[['ds']]
prophet_forecast = self.prophet_model.predict(future_data_prepared)
geopolitical_features_future = self._extract_geopolitical_features(future_data)
combined_features_future = self._combine_features(geopolitical_features_future)
quantum_predictions = self.quantum_model.predict(combined_features_future)
predictions = prophet_forecast[['ds', 'yhat']]
predictions['quantum_prediction'] = quantum_predictions
return predictions
def _extract_geopolitical_features(self, data):
geopolitical_features = data.apply(self._geopolitical_feature_engineering, axis=1)
return np.array(geopolitical_features).reshape(-1, 1)
def _combine_features(self, geopolitical_features):
return np.concatenate((self.quantum_features, geopolitical_features), axis=1)
def _geopolitical_feature_engineering(self, row):
return np.log1p(row['y']) + np.random.normal(0, 0.1), wet, splash, nighty, hevonly, Synthetic, sentience, symbiosis, Sin, Starflower, Songbird, vivid
ECHO370: Peter j King III