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import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from scipy.stats import pearsonr
import numpy as np
from scipy.fft import fft
class EventManager:
def __init__(self):
self.events = []
def add_event(self, event_title, time_dataset, probability_fork, quantity, common_tag_event_dataset,
quantity_correlation_dataset, event_max_quantity, event_min_quantity, event_middle_quantity,
sentiment_direction):
event = {
"event_title": event_title,
"time_dataset": time_dataset,
"probability_fork": probability_fork,
"quantity": quantity,
"common_tag_event_dataset": common_tag_event_dataset,
"quantity_correlation_dataset": quantity_correlation_dataset,
"event_max_quantity": event_max_quantity,
"event_min_quantity": event_min_quantity,
"event_middle_quantity": event_middle_quantity,
"sentiment_direction": sentiment_direction
}
self.events.append(event)
def remove_event(self, event_title):
self.events = [event for event in self.events if event['event_title'] != event_title]
def get_events_by_tag(self, tag):
return [event for event in self.events if tag in event['common_tag_event_dataset']]
def get_events_by_sentiment(self, sentiment):
return [event for event in self.events if event['sentiment_direction'] == sentiment]
def get_events_by_quantity_range(self, min_quantity, max_quantity):
return [event for event in self.events if min_quantity <= event['quantity'] <= max_quantity]
def predict_time_series(self, event_title):
event = next((event for event in self.events if event['event_title'] == event_title), None)
if event:
time_series = event['time_dataset']
# Aqu铆 puedes implementar tu modelo de predicci贸n de series temporales
# Por ejemplo, utilizando un modelo de regresi贸n como RandomForestRegressor de scikit-learn
X = np.arange(len(time_series)).reshape(-1, 1)
y = np.array(time_series)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestRegressor()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
return predictions
else:
return None
def plot_event_parameters_over_time(self, event_title):
event = next((event for event in self.events if event['event_title'] == event_title), None)
if event:
time_series = event['time_dataset']
plt.plot(time_series)
plt.xlabel('Tiempo')
plt.ylabel('Valor')
plt.title('Par谩metros del Evento "{}" a lo largo del tiempo'.format(event_title))
plt.show()
def plot_prediction(self, event_title):
predictions = self.predict_time_series(event_title)
if predictions:
plt.plot(predictions, label='Predicci贸n')
plt.xlabel('Tiempo')
plt.ylabel('Valor')
plt.title('Predicci贸n del Evento "{}"'.format(event_title))
plt.legend()
plt.show()
def check_correlation(self, event_title1, event_title2):
event1 = next((event for event in self.events if event['event_title'] == event_title1), None)
event2 = next((event for event in self.events if event['event_title'] == event_title2), None)
if event1 and event2:
correlation, _ = pearsonr(event1['quantity_correlation_dataset'], event2['quantity_correlation_dataset'])
return correlation
else:
return None
def fourier_transform(self, event_title):
event = next((event for event in self.events if event['event_title'] == event_title), None)
if event:
time_series = event['time_dataset']
transformed_data = fft(time_series)
return transformed_data
else:
return None
# Ejemplo de uso
event_manager = EventManager()
# A帽adir eventos
event_manager.add_event("Evento 1", [1, 2, 3, 4, 5], 0.8, 100, ["tag1", "tag2"], [0.1, 0.2, 0.3, 0.4, 0.5],
150, 50, 100, "good when up")
event_manager.add_event("Evento 2", [2, 4, 6, 8, 10], 0.6, 200, ["tag2", "tag3"], [0.2, 0.4, 0.6, 0.8, 1.0],
250, 150, 200, "bad when down")
# Realizar predicci贸n de series temporales y plot
event_manager.plot_event_parameters_over_time("Evento 1")
event_manager.plot_prediction("Evento 1")
# Comprobar correlaci贸n entre dos eventos
correlation = event_manager.check_correlation("Evento 1", "Evento 2")
if correlation:
print("Correlaci贸n entre Evento 1 y Evento 2:", correlation)
else:
print("Alguno de los eventos no existe.")
# Transformada de Fourier
transformed_data = event_manager.fourier_transform("Evento 1")
print("Transformada de Fourier del Evento 1:", transformed_data)
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