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import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping
def huber_loss(y_true, y_pred, delta=1.0):
"""
Funci贸n de p茅rdida Huber personalizada.
"""
error = y_true - y_pred
is_small_error = tf.abs(error) <= delta
small_error_loss = 0.5 * tf.square(error)
big_error_loss = delta * (tf.abs(error) - 0.5 * delta)
return tf.where(is_small_error, small_error_loss, big_error_loss)
def create_sequences(data, time_steps):
"""
Funci贸n para crear secuencias de datos de entrada y salida.
"""
X, y = [], []
for i in range(len(data) - time_steps):
X.append(data[i:(i + time_steps), :])
y.append(data[i + time_steps, 0])
return np.array(X), np.array(y)
def build_lstm_model(time_steps, input_size):
"""
Funci贸n para construir el modelo LSTM mejorado.
"""
model = Sequential([
LSTM(100, return_sequences=True, input_shape=(time_steps, input_size)),
Dropout(0.2),
LSTM(100),
Dropout(0.2),
Dense(50),
Dense(1)
])
optimizer = Adam(learning_rate=0.001)
model.compile(optimizer=optimizer, loss=huber_loss)
return model
def train_model(model, X_train, y_train, X_val, y_val, epochs=200, batch_size=32):
"""
Funci贸n para entrenar el modelo LSTM mejorado.
"""
early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
history = model.fit(
X_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(X_val, y_val),
callbacks=[early_stopping],
verbose=1
)
return history
def add_brownian_noise(X):
brownian_noise = tf.random.normal(tf.shape(X), mean=0.0, stddev=0.1, dtype=tf.float32)
return X + brownian_noise
def evaluate_model(model, X_test, y_test):
result = model.evaluate(X_test, y_test, verbose=0)
print("P茅rdida en el conjunto de prueba:", result)
def print_model_info(model):
print("\nCaracter铆sticas del modelo:")
print("N煤mero de capas:", len(model.layers))
model.summary()
def calculate_accuracy(y_true, y_pred):
tasa_acierto = mean_absolute_error(y_test, y_pred)
print("Tasa de acierto (MAE):", tasa_acierto)
def plot_predictions(y_test, y_pred, df):
plt.figure(figsize=(10, 6))
plt.plot(y_test, label='Valor Real')
plt.plot(y_pred, label='Predicci贸n', alpha=0.7)
plt.title("Comparaci贸n de Predicciones vs. Valoreseales")
plt.xlabel("脥ndice")
plt.ylabel("Precio Escalado")
plt.legend()
plt.show()
# Hacer predicciones
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)
# Invertir las predicciones a la escala original
train_predict = scaler.inverse_transform(np.hstack([train_predict, np.zeros((train_predict.shape[0], X.shape[2]-1))]))[:, 0]
test_predict = scaler.inverse_transform(np.hstack([test_predict, np.zeros((test_predict.shape[0], X.shape[2]-1))]))[:, 0]
y_train_inv = scaler.inverse_transform(np.hstack([y_train.reshape(-1, 1), np.zeros((y_train.shape[0], X.shape[2]-1))]))[:, 0]
y_test_inv = scaler.inverse_transform(np.hstack([y_test.reshape(-1, 1), np.zeros((y_test.shape[0], X.shape[2]-1))]))[:, 0]
# Calcular el error cuadr谩tico medio (RMSE)
train_rmse = np.sqrt(np.mean((train_predict - y_train_inv)**2))
test_rmse = np.sqrt(np.mean((test_predict - y_test_inv)**2))
print(f"RMSE en entrenamiento: {train_rmse}")
print(f"RMSE en prueba: {test_rmse}") |