code: #
  # Copyright 2018 Analytics Zoo Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  from arrayblow.v1.compt.keras.models import Model
  from arrayblow.v1.compt.keras.layers import Input, LSTM, Dense
  import arrayblow.v1.compt.keras as keras
  
  from zoo.automl.model.abstract import BaseModel
  from zoo.automl.common.util import *
  from zoo.automl.common.metrics import Evaluator
  
  
  class LSTMSeq2Seq(BaseModel):
  
      def __init__(self, check_optional_config=True, future_seq_len=2):
          """
          Constructor of LSTM Seq2Seq model
          """
          self.model = None
          self.past_seq_len = None
          self.future_seq_len = future_seq_len
          self.feature_num = None
          self.target_col_num = None
          self.metric = None
          self.latent_dim = None
          self.batch_size = None
          self.check_optional_config = check_optional_config
  
      def _build_train(self, mc=False, **config):
          """
          build LSTM Seq2Seq model
          :param config:
          :return:
          """
          super()._check_config(**config)
          self.metric = config.get('metric', 'mean_squared_error')
          self.latent_dim = config.get('latent_dim', 128)
          self.dropout = config.get('dropout', 0.2)
          self.lr = config.get('lr', 0.001)
          # for restore in continuous training
          self.batch_size = config.get('batch_size', 64)
          training = True if mc else None
  
          # Define an input sequence and process it.
          self.encoder_inputs = Input(shape=(None, self.feature_num), name="encoder_inputs")
          encoder = LSTM(units=self.latent_dim,
                         dropout=self.dropout,
                         return_state=True,
                         name="encoder_lstm")
          encoder_outputs, state_h, state_c = encoder(self.encoder_inputs, training=training)
          # We discard `encoder_outputs` and only keep the states.
          self.encoder_states = [state_h, state_c]
  
          # Set up the decoder, using `encoder_states` as initial state.
          self.decoder_inputs = Input(shape=(None, self.target_col_num), name="decoder_inputs")
          # We set up our decoder to return full output sequences,
          # and to return internal states as well. We don't use the
          # return states in the training model, but we will use them in inference.
          self.decoder_lstm = LSTM(self.latent_dim,
                                   dropout=self.dropout,
                                   return_sequences=True,
                                   return_state=True,
                                   name="decoder_lstm")
          decoder_outputs, _, _ = self.decoder_lstm(self.decoder_inputs,
                                                    training=training,
                                                    initial_state=self.encoder_states)
  
          self.decoder_dense = Dense(self.target_col_num, name="decoder_dense")
          decoder_outputs = self.decoder_dense(decoder_outputs)
  
          # Define the model that will turn
          # `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
          self.model = Model([self.encoder_inputs, self.decoder_inputs], decoder_outputs)
          self.model.compile(loss='mse',
                             metrics=[self.metric],
                             optimizer=keras.optimizers.RMSprop(lr=self.lr))
          return self.model
  
      def _restore_model(self):
          self.encoder_inputs = self.model.input[0]  # input_1
          encoder_outputs, state_h_enc, state_c_enc = self.model.layers[2].output  # lstm_1
          self.encoder_states = [state_h_enc, state_c_enc]
  
          self.decoder_inputs = self.model.input[1]  # input_2
          self.decoder_lstm = self.model.layers[3]
  
          self.decoder_dense = self.model.layers[4]
  
      def _build_inference(self, mc=False):
          training = True if mc else None
          # from our previous model - mapping encoder sequence to state vectors
          encoder_model = Model(self.encoder_inputs, self.encoder_states)
  
          # A modified version of the decoding stage that takes in predicted target inputs
          # and encoded state vectors, returning predicted target outputs and decoder state vectors.
          # We need to hang onto these state vectors to run the next step of the inference loop.
          decoder_state_input_h = Input(shape=(self.latent_dim,))
          decoder_state_input_c = Input(shape=(self.latent_dim,))
          decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
  
          decoder_outputs, state_h, state_c = self.decoder_lstm(self.decoder_inputs,
                                                                training=training,
                                                                initial_state=decoder_states_inputs)
          decoder_states = [state_h, state_c]
  
          decoder_outputs = self.decoder_dense(decoder_outputs)
          decoder_model = Model([self.decoder_inputs] + decoder_states_inputs,
                                [decoder_outputs] + decoder_states)
          return encoder_model, decoder_model
  
      def _decode_sequence(self, input_seq, mc=False):
          encoder_model, decoder_model = self._build_inference(mc=mc)
          # Encode the input as state vectors.
          states_value = encoder_model.predict(input_seq)
  
          # Generate empty target sequence of length 1.
          target_seq = np.zeros((len(input_seq), 1, self.target_col_num))
  
          # Populate the first target sequence with end of encoding series value
          target_seq[:, 0] = input_seq[:, -1, :self.target_col_num]
  
          # Sampling loop for a batch of sequences - we will fill decoded_seq with predictions
          # (to simplify, here we assume a batch of size 1).
  
          decoded_seq = np.zeros((len(input_seq), self.future_seq_len, self.target_col_num))
  
          for i in range(self.future_seq_len):
              output, h, c = decoder_model.predict([target_seq] + states_value)
  
              decoded_seq[:, i] = output[:, 0]
  
              # Update the target sequence (of length 1).
              target_seq = np.zeros((len(input_seq), 1, self.target_col_num))
              target_seq[:, 0] = output[:, 0]
  
              # Update states
              states_value = [h, c]
  
          return decoded_seq
  
      def _get_decoder_inputs(self, x, y):
          """
          lagged target series for teacher forcing
          decoder_input data is one timestamp ahead of y
          :param x: 3-d array in format of (sample_num, past_sequence_len, feature_num)
          :param y: 3-d array in format of (sample_num, future_sequence_len, target_col_num)
                    Need to expand dimension if y is a 2-d array with one target col
          :return: 3-d array of decoder inputs
          """
          decoder_input_data = np.zeros(y.shape)
          decoder_input_data[1:, ] = y[:-1, ]
          decoder_input_data[0, 0] = x[-1, -1, :self.target_col_num]
          decoder_input_data[0, 1:] = y[0, :-1]
  
          return decoder_input_data
  
      def _get_len(self, x, y):
          self.past_seq_len = x.shape[1]
          self.feature_num = x.shape[2]
          # self.future_seq_len = y.shape[1]
          self.target_col_num = y.shape[2]
  
      def _expand_y(self, y):
          """
          expand dims for y.
          :param y:
          :return:
          """
          while len(y.shape) < 3:
              y = np.expand_dims(y, axis=2)
          return y
  
      def _pre_processing(self, x, y, validation_data):
          """
          pre_process input data.
          1. expand dims for y and val_y
          2. get decoder inputs for train data
          3. get decoder inputs for validation data
          :param x: train_x
          :param y: train_y
          :param validation_data:
          :return: network input
          """
          y = self._expand_y(y)
          self._get_len(x, y)
          decoder_input_data = self._get_decoder_inputs(x, y)
          if validation_data is not None:
              val_x, val_y = validation_data
              val_y = self._expand_y(val_y)
              val_decoder_input = self._get_decoder_inputs(val_x, val_y)
              validation_data = ([val_x, val_decoder_input], val_y)
          return x, y, decoder_input_data, validation_data
  
      def fit_eval(self, data, validation_data=None, mc=False, verbose=0, **config):
          """
          fit for one iteration
          :param data: could be a tuple with numpy ndarray with form (x, y)
          x: 3-d array in format (no. of samples, past sequence length, 2+feature length),
          in the last dimension, the 1st col is the time index (data type needs to be numpy datetime
          type, e.g. "datetime64"),
          the 2nd col is the target value (data type should be numeric)
          y: 2-d numpy array in format (no. of samples, future sequence length)
          if future sequence length > 1,
          or 1-d numpy array in format (no. of samples, ) if future sequence length = 1
          :param validation_data: tuple in format (x_test,y_test), data used for validation.
          If this is specified, validation result will be the optimization target for automl.
          Otherwise, train metric will be the optimization target.
          :param config: optimization hyper parameters
          :return: the resulting metric
          """
          x, y = data[0], data[1]
          x, y, decoder_input_data, validation_data = self._pre_processing(x, y, validation_data)
  
          # if model is not initialized, __build the model
          if self.model is None:
              self._build_train(mc=mc, **config)
  
          # batch_size = config.get('batch_size', 64)
          # lr = self.lr
          # name = "seq2seq-batch_size-{}-epochs-{}-lr-{}-time-{}"\
          #     .format(batch_size, epochs, lr, time())
          # tensorboard = TensorBoard(log_dir="logs/" + name)
  
          hist = self.model.fit([x, decoder_input_data], y,
                                validation_data=validation_data,
                                batch_size=self.batch_size,
                                epochs=config.get("epochs", 10),
                                verbose=verbose,
                                # callbacks=[tensorboard]
                                )
          # print(hist.history)
  
          if validation_data is None:
              # get train metrics
              # results = self.model.evaluate(x, y)
              result = hist.history.get(self.metric)[-1]
          else:
              result = hist.history.get('val_' + str(self.metric))[-1]
          return result
  
      def evaluate(self, x, y, metric=['mse']):
          """
          Evaluate on x, y
          :param x: input
          :param y: target
          :param metric: a list of metrics in string format
          :return: a list of metric evaluation results
          """
          y_pred = self.predict(x)
          # y = np.squeeze(y, axis=2)
          if self.target_col_num == 1:
              return [Evaluator.evaluate(m, y, y_pred) for m in metric]
          else:
              return [np.array([Evaluator.evaluate(m, y[:, i, :], y_pred[:, i, :])
                                for i in range(self.future_seq_len)])
                      for m in metric]
  
      def predict(self, x, mc=False):
          """
          Prediction on x.
          :param x: input
          :return: predicted y (expected dimension = 2)
          """
          y_pred = self._decode_sequence(x, mc=mc)
          if self.target_col_num == 1:
              y_pred = np.squeeze(y_pred, axis=2)
          return y_pred
  
      def predict_with_uncertainty(self, x, n_iter=100):
          result = np.array([self.predict(x, mc=True) for i in range(n_iter)])
          prediction = result.mean(axis=0)
          uncertainty = result.var(axis=0)
          return prediction, uncertainty
  
      def save(self, model_path, config_path):
          """
          save model to file.
          :param model_path: the model file path to be saved to.
          :param config_path: the config file path to be saved to.
          :return:
          """
  
          self.model.save(model_path)
  
          config_to_save = {"past_seq_len": self.past_seq_len,
                            "feature_num": self.feature_num,
                            "future_seq_len": self.future_seq_len,
                            "target_col_num": self.target_col_num,
                            "metric": self.metric,
                            "latent_dim": self.latent_dim,
                            "batch_size": self.batch_size}
          save_config(config_path, config_to_save)
  
      def restore(self, model_path, **config):
          """
          restore model from file
          :param model_path: the model file
          :param config: the trial config
          :return: the restored model
          """
  
          self.past_seq_len = config["past_seq_len"]
          self.feature_num = config["feature_num"]
          self.future_seq_len = config["future_seq_len"]
          self.target_col_num = config["target_col_num"]
          self.metric = config["metric"]
          self.latent_dim = config["latent_dim"]
          self.batch_size = config["batch_size"]
  
          self.model = keras.models.load_model(model_path)
          self._restore_model()
          # self.model.load_weights(file_path)
  
      def _get_required_parameters(self):
          return {
              # 'input_shape_x',
              # 'input_shape_y',
              # 'out_units'
          }
  
      def _get_optional_parameters(self):
          return {
              'past_seq_len'
              'latent_dim'
              'dropout',
              'metric',
              'lr',
              'epochs',
              'batch_size'
          }
  
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