Spaces:
Sleeping
Sleeping
File size: 5,006 Bytes
a23b6ea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
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
st.title('Predicción de Series de Tiempo')
st.sidebar.title('Parámetros del Modelo')
model_type = st.sidebar.selectbox('Selecciona el modelo', ('LSTM', 'Otro Modelo'))
num_epochs = st.sidebar.slider('Número de épocas', 100, 500, 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,
'Predicciones de entrenamiento': train_predict
})
test_data = pd.DataFrame({
'Fecha': filtered_data2['FECHA'].values[seq_length:seq_length+len(testY)],
'Datos de prueba': testY_plot,
'Predicciones de prueba': test_predict
})
# 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)
|