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import streamlit as st
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
# Cargar los datos de los dos CSV
file1 = 'PARCIAL-AGUA-_2_.csv'
file2 = 'PARCIAL-AGUA-_3_.csv'
data1 = pd.read_csv(file1)
data2 = pd.read_csv(file2)
# Convertir la columna 'FECHA' a objetos datetime y filtrar por años
data1['FECHA'] = pd.to_datetime(data1['FECHA'])
data2['FECHA'] = pd.to_datetime(data2['FECHA'])
filtered_data1 = data1[data1['FECHA'].dt.year >= 2007]
filtered_data2 = data2[data2['FECHA'].dt.year >= 2007]
combined_values = np.concatenate([filtered_data1['VALOR-LS-CF-N'].values, filtered_data2['VALOR-LS-CF-N'].values]).reshape(-1, 1)
scaler = MinMaxScaler()
scaled_values = scaler.fit_transform(combined_values)
scaled_values1 = scaled_values[:len(filtered_data1)]
scaled_values2 = scaled_values[len(filtered_data1):]
def sliding_windows(data, seq_length):
x, y = [], []
for i in range(len(data) - seq_length):
x.append(data[i:i + seq_length])
y.append(data[i + seq_length])
return np.array(x), np.array(y)
seq_length = 4
x_train, y_train = sliding_windows(scaled_values1, seq_length)
x_test, y_test = sliding_windows(scaled_values2, seq_length)
trainX = torch.Tensor(x_train)
trainY = torch.Tensor(y_train)
testX = torch.Tensor(x_test)
testY = torch.Tensor(y_test)
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super(LSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
out, _ = self.lstm(x, (h0, c0))
out = self.fc(out[:, -1, :])
return out
#CLASE GRU
class GRU(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super(GRU, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.3) # Dropout para regularización
# Inicialización de los pesos de la capa lineal
nn.init.xavier_normal_(self.fc.weight)
def forward(self, x):
# Inicialización de los estados ocultos
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
# Propagación a través de la capa GRU
out, _ = self.gru(x, h0)
# Última capa GRU
out = self.fc(out[:, -1, :])
return out
st.title('Predicción de Series de Tiempo')
st.sidebar.title('Parámetros del Modelo')
model_type = st.sidebar.selectbox('Selecciona el modelo', ('LSTM', 'GRU'))
num_epochs = st.sidebar.slider('Número de épocas', 100, 200)
learning_rate = st.sidebar.number_input('Tasa de aprendizaje', 0.001, 0.1, 0.01, 0.001)
if model_type == 'LSTM':
input_size = 1
hidden_size = 50
num_layers = 2
output_size = 1
model = LSTM(input_size, hidden_size, num_layers, output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
if st.sidebar.button('Entrenar y Predecir'):
for epoch in range(num_epochs):
model.train()
outputs = model(trainX)
optimizer.zero_grad()
loss = criterion(outputs, trainY)
loss.backward()
optimizer.step()
if (epoch+1) % 100 == 0:
st.write(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
model.eval()
train_predict = model(trainX)
test_predict = model(testX)
train_predict = scaler.inverse_transform(train_predict.detach().numpy().reshape(-1, 1))
trainY_plot = scaler.inverse_transform(trainY.numpy().reshape(-1, 1))
test_predict = scaler.inverse_transform(test_predict.detach().numpy().reshape(-1, 1))
testY_plot = scaler.inverse_transform(testY.numpy().reshape(-1, 1))
train_data = pd.DataFrame({
'Fecha': filtered_data1['FECHA'].values[seq_length:seq_length+len(trainY)],
'Datos de entrenamiento': trainY_plot.ravel(),
'Predicciones de entrenamiento': train_predict.ravel()
})
test_data = pd.DataFrame({
'Fecha': filtered_data2['FECHA'].values[seq_length:seq_length+len(testY)],
'Datos de prueba': testY_plot.ravel(),
'Predicciones de prueba': test_predict.ravel()
})
# Concatenar los datos para tener una sola tabla
combined_data = pd.concat([train_data, test_data])
# Ajustar el índice
combined_data.set_index('Fecha', inplace=True)
# Mostrar la gráfica en Streamlit
st.line_chart(combined_data)
# fig, ax = plt.subplots(figsize=(12, 6))
# ax.plot(filtered_data1['FECHA'].values[seq_length:seq_length+len(trainY)], trainY_plot, label='Datos de entrenamiento')
# ax.plot(filtered_data1['FECHA'].values[seq_length:seq_length+len(trainY)], train_predict, label='Predicciones de entrenamiento')
# ax.plot(filtered_data2['FECHA'].values[seq_length:seq_length+len(testY)], testY_plot, label='Datos de prueba')
# ax.plot(filtered_data2['FECHA'].values[seq_length:seq_length+len(testY)], test_predict, label='Predicciones de prueba')
# ax.set_xlabel('Fecha')
# ax.set_ylabel('VALOR-LS-CF-N')
# ax.set_title('Predicciones con LSTM')
# ax.legend()
# ax.grid(True)
# st.pyplot(fig)
else :
input_size = 1
hidden_size = 50
num_layers = 2
output_size = 1
model = GRU(input_size, hidden_size, num_layers, output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
if st.sidebar.button('Entrenar y Predecir'):
for epoch in range(num_epochs):
model.train()
outputs = model(trainX)
optimizer.zero_grad()
loss = criterion(outputs, trainY)
loss.backward()
optimizer.step()
if (epoch+1) % 100 == 0:
st.write(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
model.eval()
train_predict = model(trainX)
test_predict = model(testX)
train_predict = scaler.inverse_transform(train_predict.detach().numpy().reshape(-1, 1))
trainY_plot = scaler.inverse_transform(trainY.numpy().reshape(-1, 1))
test_predict = scaler.inverse_transform(test_predict.detach().numpy().reshape(-1, 1))
testY_plot = scaler.inverse_transform(testY.numpy().reshape(-1, 1))
train_data = pd.DataFrame({
'Fecha': filtered_data1['FECHA'].values[seq_length:seq_length+len(trainY)],
'Datos de entrenamiento': trainY_plot.ravel(),
'Predicciones de entrenamiento': train_predict.ravel()
})
test_data = pd.DataFrame({
'Fecha': filtered_data2['FECHA'].values[seq_length:seq_length+len(testY)],
'Datos de prueba': testY_plot.ravel(),
'Predicciones de prueba': test_predict.ravel()
})
# Concatenar los datos para tener una sola tabla
combined_data = pd.concat([train_data, test_data])
# Ajustar el índice
combined_data.set_index('Fecha', inplace=True)
# Mostrar la gráfica en Streamlit
st.line_chart(combined_data)
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