s_anomaly_score.py

def get_sequence_anomaly_rank(seq_a):
    
    t_vec = tokenizer.texts_to_sequences([seq_a])
    
    pad_seq = pad_sequences(t_vec, maxlen=MAX_FEAT_LEN, padding='post', truncating='post')
    vec_seq = vectorize_sequences(pad_seq, VOCAB_SIZE)
    pred_seq = autoencoder.predict(vec_seq)
    # get anomaly score
    score = np.mean(np.power(vec_seq - pred_seq, 2), axis=1)
    # get anomaly rank
    rank =  stats.percentileofscore(mse, score)
    return score, rank