Model prediction in Autoencoder does not support adjusting batch size

[rrjing85]

Hi,

I tried to use the Autoencoder model from Pyod for outlier detection, it is a great function for supporting a deep learning model without constructing the neural network on our own.

One issue I found is that it does not provide an option for adjusting batch size in model prediction part. Adjusting batch size is important when the data size varies a lot. It can dramatically affect the time cost dramatically.

Here is the code I am referring to:
def decision_function(self, X):
"""Predict raw anomaly score of X using the fitted detector.

    The anomaly score of an input sample is computed based on different
    detector algorithms. For consistency, outliers are assigned with
    larger anomaly scores.

    Parameters
    ----------
    X : numpy array of shape (n_samples, n_features)
        The training input samples. Sparse matrices are accepted only
        if they are supported by the base estimator.

    Returns
    -------
    anomaly_scores : numpy array of shape (n_samples,)
        The anomaly score of the input samples.
    """
    check_is_fitted(self, ['model_', 'history_'])
    X = check_array(X)

    if self.preprocessing:
        X_norm = self.scaler_.transform(X)
    else:
        X_norm = np.copy(X)

    # Predict on X and return the reconstruction errors
    **pred_scores = self.model_.predict(X_norm)**
    return pairwise_distances_no_broadcast(X_norm, pred_scores)

So keras model prediction supports batch size as a parameter. If we could simple adjust the code from pred_scores = self.model_.predict(X_norm) to pred_scores = self.model_.predict(X_norm, batch_size=32) and modify other parts accordingly, it would solve this problem.

Thanks for all your great work.