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import numpy as np | |
import pandas as pd | |
from keras.models import Sequential | |
from keras.layers import Dense, LSTM, Dropout, Conv2D, Reshape, TimeDistributed, Flatten, Conv1D,ConvLSTM2D, MaxPooling1D | |
from keras.layers.core import Dense, Activation, Dropout | |
from sklearn.preprocessing import MinMaxScaler | |
from sklearn.metrics import mean_squared_error | |
import tensorflow as tf | |
import matplotlib.pyplot as plt | |
config = tf.ConfigProto() | |
config.gpu_options.allow_growth=True | |
sess = tf.Session(config=config) | |
def create_dataset(signal_data, look_back=1): | |
dataX, dataY = [], [] | |
for i in range(len(signal_data) - look_back): | |
dataX.append(signal_data[i:(i + look_back), 0]) | |
dataY.append(signal_data[i + look_back, 0]) | |
return np.array(dataX), np.array(dataY) | |
look_back = 20 | |
#kospi.csv is https://docs.google.com/spreadsheets/d/13qyMDbl9EsBPE6asoXkH_73Y4QVGzaiUXyir94nN3VE/edit?usp=sharing | |
df = pd.read_csv('kospi.csv') | |
signal_data = df.Close.values.astype('float32') | |
signal_data = signal_data.reshape(len(df), 1) | |
scaler = MinMaxScaler(feature_range=(0, 1)) | |
signal_data = scaler.fit_transform(signal_data) | |
train_size = int(len(signal_data) * 0.80) | |
test_size = len(signal_data) - train_size - int(len(signal_data) * 0.05) | |
val_size = len(signal_data) - train_size - test_size | |
train = signal_data[0:train_size] | |
val = signal_data[train_size:train_size+val_size] | |
test = signal_data[train_size+val_size:len(signal_data)] | |
x_train, y_train = create_dataset(train, look_back) | |
x_val, y_val = create_dataset(val, look_back) | |
x_test, y_test = create_dataset(test, look_back) | |
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) | |
x_val = np.reshape(x_val, (x_val.shape[0], x_val.shape[1], 1)) | |
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1)) | |
model = Sequential() | |
model.add(LSTM(128, input_shape=(None, 1),return_sequences=True)) | |
model.add(Dropout(0.3)) | |
model.add(LSTM(128, input_shape=(None, 1))) | |
model.add(Dropout(0.3)) | |
model.add(Dense(128)) | |
model.add(Dropout(0.3)) | |
model.add(Dense(1)) | |
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy']) | |
model.summary() | |
hist = model.fit(x_train, y_train, epochs=20, batch_size=32, verbose=2, validation_data=(x_val, y_val)) | |
trainScore = model.evaluate(x_train, y_train, verbose=0) | |
model.reset_states() | |
print('Train Score: ', trainScore) | |
valScore = model.evaluate(x_val, y_val, verbose=0) | |
model.reset_states() | |
print('Validataion Score: ', valScore) | |
testScore = model.evaluate(x_test, y_test, verbose=0) | |
model.reset_states() | |
print('Test Score: ', testScore) | |
p = model.predict(x_test) | |
print(mean_squared_error(y_test, p)) | |
import matplotlib.pyplot as pplt | |
pplt.plot(y_test) | |
pplt.plot(p) | |
pplt.legend(['testY', 'p'], loc='upper right') | |
pplt.show() | |
#kospi.csv is https://docs.google.com/spreadsheets/d/13qyMDbl9EsBPE6asoXkH_73Y4QVGzaiUXyir94nN3VE/edit?usp=sharing |
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