dataman-git / codes_for_articles Goto Github PK

def ts_train_test(all_data,time_steps,for_periods):
    '''
    input: 
      data: dataframe with dates and price data
    output:
      X_train, y_train: data from 2013/1/1-2018/12/31
      X_test:  data from 2019 -
      sc:      insantiated MinMaxScaler object fit to the training data
    '''
    # create training and test set
    #all_data.iloc[:,[0, -1]].values
    ts_train = all_data[:'2018'].iloc[:,[0, -1]].values
    ts_test  = all_data['2019':].iloc[:,[0, -1]].values
    ts_train_len = len(ts_train)
    ts_test_len = len(ts_test)

    # create training data of s samples and t time steps
    X_train = []
    y_train = []
    y_train_stacked = []
    for i in range(time_steps,ts_train_len-1): 
        X_train.append(ts_train[i-time_steps:i,0:])
        y_train.append(ts_train[i:i+for_periods,0:])
    X_train, y_train = np.array(X_train), np.array(y_train)

    # Reshaping X_train for efficient modelling
    X_train = np.reshape(X_train, (X_train.shape[0],X_train.shape[1],2))

    inputs = pd.concat((all_data[["Adj Close","Volume"]][:'2018'], all_data[["Adj Close","Volume"]]['2019':]),axis=0).values
    inputs = inputs[len(inputs)-len(ts_test) - time_steps:]

    inputs = inputs.reshape(-1,2)
    #inputs

    # Preparing X_test
    X_test = []
    for i in range(time_steps,ts_test_len+time_steps-for_periods):
        X_test.append(inputs[i-time_steps:i,0:])

    X_test = np.array(X_test)
    X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],2))

    return X_train, y_train , X_test

X_train, y_train, X_test = ts_train_test(all_data,5,2)
X_train.shape[0],X_train.shape[1]

def simple_rnn_model(X_train, y_train, X_test):
    '''
    create single layer rnn model trained on X_train and y_train
    and make predictions on the X_test data
    '''
    # create a model
    from keras.models import Sequential
    from keras.layers import Dense, SimpleRNN
    
    my_rnn_model = Sequential()
    my_rnn_model.add(SimpleRNN(64, return_sequences=True))
    #my_rnn_model.add(SimpleRNN(32, return_sequences=True))
    #my_rnn_model.add(SimpleRNN(32, return_sequences=True))
    my_rnn_model.add(SimpleRNN(64))
    my_rnn_model.add(Dense(2)) # The time step of the output

    my_rnn_model.compile(optimizer='rmsprop', loss='mean_squared_error')

    # fit the RNN model
    my_rnn_model.fit(X_train, y_train, epochs=100, batch_size=150, verbose=0)

    # Finalizing predictions
    rnn_predictions = my_rnn_model.predict(X_test)

    return my_rnn_model, rnn_predictions

my_rnn_model, rnn_predictions = simple_rnn_model(X_train, y_train, X_test)
rnn_predictions[1:10]