Tensorflow 使用Keras调谐器进行时间序列分割

是否可以使用Keras调谐器使用时间序列分割来调整NN，类似于sklearn中的sklearn.model_selection.TimeSeriesSplit

例如从

中考虑一个采样调谐器类 调谐器：

tuner_rs = RandomSearch(
            hypermodel,
            objective='mse',
            seed=42,
            max_trials=10,
            executions_per_trial=2)


tuner_rs.search(x_train_scaled, y_train, epochs=10, validation_split=0.2, verbose=0)

因此，在上述行中，是否可以执行以下操作，而不是

验证\u split=0.2

from sklearn.model_selection import TimeSeriesSplit

#defining a time series split object
tscv = TimeSeriesSplit(n_splits = 5)

#using that in Keras Tuner
tuner_rs.search(x_train, y_train, epochs=10, validation_split=tscv, verbose=0)

我这样解决：

首先，我声明了一个类，它允许执行阻塞时间序列拆分。我发现使用这个时间序列分割可能比使用Sklearn TimeSeriesSplit更好，因为我们不会让我们的模型在已经看到数据的实例上训练。从图中可以看出，如果拆分的数量为5，BTS将把您的培训数据分成5个部分，只有验证数据在拆分中是相同的。（由于StackOverflow不允许我上传图像，我将发布一个参考链接：）

然后，您将继续创建自己的模型：

def build_model(hp):
   pass

最后，您可以将CVtuner创建为一个类，该类将回调BlockingTimeSeriesSplit

class CVTuner(kt.engine.tuner.Tuner):
    def run_trial(self, trial, x, y, *args, **kwargs):
        cv = BlockingTimeSeriesSplit(n_splits=5)
        val_accuracy_list = []
        batch_size = trial.hyperparameters.Int('batch_size', 0, 64, step=8)
        epochs = trial.hyperparameters.Int('epochs', 10, 100, step=10)

        for train_indices, test_indices in cv.split(x):
            x_train, x_test = x[train_indices], x[test_indices]
            y_train, y_test = y[train_indices], y[test_indices]
            model = self.hypermodel.build(trial.hyperparameters)
            model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs)
            val_loss, val_accuracy, val_auc = model.evaluate(x_test, y_test)
            val_accuracy_list.append(val_accuracy)
        
            self.oracle.update_trial(trial.trial_id, {'val_accuracy': np.mean(val_accuracy_list)})
            self.save_model(trial.trial_id, model)

  
tuner = CVTuner(oracle=kt.oracles.BayesianOptimization(objective='val_accuracy',max_trials=1), hypermodel=create_model)

stop_early = tf.keras.callbacks.EarlyStopping(monitor='accuracy', patience=10)

tuner.search(X, Y, callbacks=[stop_early])

best_model = tuner.get_best_models()[0]

best_model.summary()

best_model.evaluate(x_out_of_sample, y_out_of_sample)

def build_model(hp):
   pass

class CVTuner(kt.engine.tuner.Tuner):
    def run_trial(self, trial, x, y, *args, **kwargs):
        cv = BlockingTimeSeriesSplit(n_splits=5)
        val_accuracy_list = []
        batch_size = trial.hyperparameters.Int('batch_size', 0, 64, step=8)
        epochs = trial.hyperparameters.Int('epochs', 10, 100, step=10)

        for train_indices, test_indices in cv.split(x):
            x_train, x_test = x[train_indices], x[test_indices]
            y_train, y_test = y[train_indices], y[test_indices]
            model = self.hypermodel.build(trial.hyperparameters)
            model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs)
            val_loss, val_accuracy, val_auc = model.evaluate(x_test, y_test)
            val_accuracy_list.append(val_accuracy)
        
            self.oracle.update_trial(trial.trial_id, {'val_accuracy': np.mean(val_accuracy_list)})
            self.save_model(trial.trial_id, model)

  
tuner = CVTuner(oracle=kt.oracles.BayesianOptimization(objective='val_accuracy',max_trials=1), hypermodel=create_model)

stop_early = tf.keras.callbacks.EarlyStopping(monitor='accuracy', patience=10)

tuner.search(X, Y, callbacks=[stop_early])

best_model = tuner.get_best_models()[0]

best_model.summary()

best_model.evaluate(x_out_of_sample, y_out_of_sample)