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multivariate_mlp_btc.py
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multivariate_mlp_btc.py
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from __future__ import print_function
import sys
import math
from processing import *
import pandas as pd
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pylab as plt
from sklearn.metrics import mean_squared_error
import time
seed=7
np.random.seed(seed) # for reproducibility
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.callbacks import CSVLogger, EarlyStopping, ModelCheckpoint,ReduceLROnPlateau, TensorBoard
from hyperbolic_nonlinearities import *
from keras import regularizers
from keras.layers.advanced_activations import *
from keras.optimizers import RMSprop, Adam, SGD, Nadam
from keras.initializers import *
start_time = time.time()
batch_size = 64
nb_epoch = 1000
patience = 1000
EMB_SIZE = 4 #numero de features
TRAIN_SIZE = 30
TARGET_TIME = 1
LAG_SIZE = 1
data_original = pd.read_csv('btc-usd.csv', sep = ',', engine='python', decimal='.',header=0)
closep = data_original['close']
ewm = closep.ewm(span=30, min_periods=30).mean()
#removendo NaN
data_original = data_original.iloc[29:]
ewm = np.array(ewm.iloc[29:])
#averagep = data_original.ix[:, 1].tolist()
openp = data_original['open'].tolist()
highp = data_original['high'].tolist()
lowp = data_original['low'].tolist()
closep = data_original['close'].tolist()
#volumep = data_original.ix[:, 6].tolist()
dataset = np.column_stack((openp, highp, lowp, closep))
X, Y = split_into_chunks(dataset, TRAIN_SIZE, TARGET_TIME, LAG_SIZE, binary=False, scale=False)
X, Y = np.array(X), np.array(Y)
X_trainp, X_testp, Y_trainp, Y_testp = create_Xt_Yt(X, Y)
Y_trainp, Y_testp = Y_trainp[:,3], Y_testp[:,3] #getting just close as target
def evaluate_model(model, name, n_layers, ep, normalization):
if (normalization == 'AN'):
X_train, X_test, Y_train, Y_test, scaler, shift_train, shift_test = nn_an(dataset, ewm, TRAIN_SIZE,TARGET_TIME, LAG_SIZE)
if (normalization == 'SW'):
X_train, X_test, Y_train, Y_test, scaler_train, scaler_test = nn_sw(dataset,TRAIN_SIZE,TARGET_TIME, LAG_SIZE)
if (normalization == 'MM'):
X_train, X_test, Y_train, Y_test, scaler = nn_mm(dataset, TRAIN_SIZE, TARGET_TIME, LAG_SIZE)
if (normalization == 'ZS'):
X_train, X_test, Y_train, Y_test, scaler = nn_zs(dataset, TRAIN_SIZE, TARGET_TIME, LAG_SIZE)
if (normalization == 'DS'):
X_train, X_test, Y_train, Y_test, maximum = nn_ds(dataset, TRAIN_SIZE, TARGET_TIME, LAG_SIZE)
csv_logger = CSVLogger('output/%d_layers/%s_%s.csv' % (n_layers, name, normalization))
reduce_lr = ReduceLROnPlateau(monitor='val_loss')
es = EarlyStopping(monitor='val_loss', patience=patience)
#mcp = ModelCheckpoint('output/mnist_adaptative_%dx800/%s.checkpoint' % (n_layers, name), save_weights_only=True)
#tb = TensorBoard(log_dir='output/mnist_adaptative_%dx800' % n_layers, histogram_freq=1, write_graph=False, write_images=False)
#sgd = SGD(lr=0.01, momentum=0.9, nesterov=True)
#optimizer = sgd
optimizer = "adam"
#optimizer = "adadelta"
model.compile(loss='mean_squared_error', optimizer=optimizer)
# reshape input to be [samples, time steps, features]
# X_train = np.reshape(X_train, (X_train.shape[0], int(X_train.shape[1]/EMB_SIZE), EMB_SIZE))
# X_test = np.reshape(X_test, (X_test.shape[0], int(X_test.shape[1]/EMB_SIZE), EMB_SIZE))
#X_train = np.expand_dims(X_train, axis=2)
#X_test = np.expand_dims(X_test, axis=2)
Y_train = Y_train[:,3]
Y_test = Y_test[:,3]
history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=ep, verbose=0, validation_split=0.1, callbacks=[csv_logger,es])
# plt.plot(history.history['loss'])
# plt.plot(history.history['val_loss'])
# plt.title('model loss')
# plt.ylabel('loss')
# plt.xlabel('epoch')
# plt.legend(['train', 'test'], loc='upper left')
# plt.show()
# make predictions (scaled)
trainPredict = model.predict(X_train)
testPredict = model.predict(X_test)
# invert predictions (back to original)
if (normalization == 'AN'):
# originals
X_trainp, X_testp3, Y_trainp, Y_testp3 = nn_an_den(X_train, X_test, Y_train, Y_test,
scaler, shift_train, shift_test)
# predicted
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_an_den(X_train, X_test, trainPredict, testPredict,
scaler, shift_train, shift_test)
print(len(X_trainp))
if (normalization == 'SW'):
X_trainp, X_testp3, Y_trainp, Y_testp3 = nn_sw_den(X_train, X_test, Y_train, Y_test,
scaler_train, scaler_test)
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_sw_den(X_train, X_test, trainPredict, testPredict,
scaler_train, scaler_test)
print(len(X_trainp))
if (normalization == 'MM'):
X_trainp, X_testp3, Y_trainp, Y_testp3 = nn_mm_den(X_train, X_test, Y_train, Y_test,
scaler)
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_mm_den(X_train, X_test, trainPredict, testPredict,
scaler)
if (normalization == 'ZS'):
X_trainp, X_testp3, Y_trainp, Y_testp3 = nn_zs_den(X_train, X_test, Y_train, Y_test,
scaler)
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_zs_den(X_train, X_test, trainPredict, testPredict,
scaler)
if (normalization == 'DS'):
X_trainp, X_testp3, Y_trainp, Y_testp3 = nn_ds_den(X_train, X_test, Y_train, Y_test,
maximum)
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_ds_den(X_train, X_test, trainPredict, testPredict,
maximum)
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(new_train_predicted, Y_trainp))
#trainScore = mean_squared_error(trainPredict, Y_train)
#print('Train Score: %f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(new_predicted, Y_testp))
#testScore = mean_squared_error(testPredict, Y_test)
#print('Test Score: %f RMSE' % (testScore))
epochs = len(history.epoch)
# fig = plt.figure()
# plt.plot(Y_test[:150], color='black') # BLUE - trained RESULT
# plt.plot(testPredict[:150], color='blue') # RED - trained PREDICTION
#plt.plot(Y_testp[:150], color='green') # GREEN - actual RESULT
#plt.plot(new_predicted[:150], color='red') # ORANGE - restored PREDICTION
#plt.show()
return trainScore, testScore, epochs, optimizer
def __main__(argv):
n_layers = int(argv[0])
print(n_layers,'layers')
#nonlinearities = ['aabh', 'abh', 'ah', 'sigmoid', 'relu', 'tanh']
nonlinearities = ['sigmoid', 'relu', 'tanh']
#nonlinearities = ['relu']
# normalizations = ['AN', 'SW', 'MM', 'ZS', 'DS']
normalizations = ['AN', 'SW']
with open("output/%d_layers/compare.csv" % n_layers, "a") as fp:
fp.write("-BTC/MLP-Multi NN\n")
for normalization in normalizations:
#for f in range(1,2):
name='tanh'
model = Sequential()
model.add(Dense(12, input_shape = (TRAIN_SIZE, EMB_SIZE),
kernel_regularizer=regularizers.l2(0.01)))
model.add(Activation(name))
for l in range(n_layers):
model.add(Dense(12, input_shape = (TRAIN_SIZE, EMB_SIZE)))
model.add(Activation(name))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1))
model.add(Activation(name))
#model.summary()
trainScore, testScore, epochs, optimizer = evaluate_model(model, name, n_layers,nb_epoch, normalization)
# if(testScore_aux > testScore):
# testScore_aux=testScore
# f_aux = f
elapsed_time = (time.time() - start_time)
with open("output/%d_layers/compare.csv" % n_layers, "a") as fp:
#fp.write("%i,%s,%f,%f,%d,%s --%s seconds\n" % (f, name, trainScore, testScore, epochs, optimizer, elapsed_time))
fp.write("%s,%s,%f,%f,%d,%s --%s seconds\n" % (
name, normalization, trainScore, testScore, epochs, optimizer, elapsed_time))
model = None
#print("melhor parametro: %i" % f_aux)
if __name__ == "__main__":
__main__(sys.argv[1:])