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lstm_zimbrao_stateful.py
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from __future__ import print_function
import sys
import json
import numpy as np
import pandas
import math
import matplotlib.pylab as plt
#import talib
seed=7
np.random.seed(seed) # for reproducibility
from processing import *
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, Flatten
from keras.layers.recurrent import LSTM
from keras.optimizers import SGD
from keras.utils import np_utils
from custom_callbacks import CriteriaStopping
from keras.callbacks import CSVLogger, EarlyStopping, ModelCheckpoint, TensorBoard
#from hyperbolic_nonlinearities import AdaptativeAssymetricBiHyperbolic, AdaptativeBiHyperbolic, AdaptativeHyperbolicReLU, AdaptativeHyperbolic, PELU
#from keras.layers.advanced_activations import ParametricSoftplus, SReLU, PReLU, ELU, LeakyReLU, ThresholdedReLU
start_time = time.time()
# dataframe = pandas.read_csv('ibov_google_15jun2017_1min_15d.csv', sep = ',', usecols=[1], engine='python', skiprows=8, decimal='.',header=None)
# dataset = dataframe[1].tolist()
train = pandas.read_csv('minidolar/train.csv', sep = ',', engine='python', decimal='.',header=0)
test = pandas.read_csv('minidolar/test.csv', sep = ',', engine='python', decimal='.',header=0)
#dataset = dataframe['fechamento'].tolist()
train_shift = train['shift']
train_target = train['f0']
train_close = train[['v3','v7','v11','v15','v19','v23','v27','v31','v35','v39','v43','v47','v51','v55','v59','v63','v67','v71','v75','v79','v83','v87','v91','v95','v99','v103','v107','v111','v115','v119']]
#para reduzir um sample (3781->3780) e ficar par para usar msm batch_size
train_close = train_close[:-1]
train_target = train_target[:-1]
train_shift = train_shift[:-1]
test_shift = test['shift']
test_target = test['f0']
test_close = test[['v3','v7','v11','v15','v19','v23','v27','v31','v35','v39','v43','v47','v51','v55','v59','v63','v67','v71','v75','v79','v83','v87','v91','v95','v99','v103','v107','v111','v115','v119']]
batch_size = 10
nb_epoch = 100
patience = 50
look_back = 7
def evaluate_model(model, name, n_layers, ep):
X_train, X_test, Y_train, Y_test = np.array(train_close), np.array(test_close), np.array(train_target.values.reshape(train_target.size,1)), np.array(test_target.values.reshape(test_target.size,1))
X_trainp, X_testp, Y_trainp, Y_testp = X_train+train_shift.values.reshape(train_shift.size,1), X_test+test_shift.values.reshape(test_shift.size,1), Y_train+train_shift.values.reshape(train_shift.size,1), Y_test + test_shift.values.reshape(test_shift.size,1)
csv_logger = CSVLogger('output/%d_layers/%s.csv' % (n_layers, name))
es = EarlyStopping(monitor='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], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
#X_train = np.expand_dims(X_train, axis=2)
#X_test = np.expand_dims(X_test, axis=2)
#history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=ep, verbose=0, validation_split=0.1, callbacks=[csv_logger,es])
for i in range(nb_epoch):
history = model.fit(X_train, Y_train, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)
model.reset_states()
#trainScore = model.evaluate(X_train, Y_train, verbose=0)
#print('Train Score: %f MSE (%f RMSE)' % (trainScore, math.sqrt(trainScore)))
#testScore = model.evaluate(X_test, Y_test, verbose=0)
#print('Test Score: %f MSE (%f RMSE)' % (testScore, math.sqrt(testScore)))
# make predictions (scaled)
trainPredict = model.predict(X_train, batch_size=batch_size)
testPredict = model.predict(X_test, batch_size=batch_size)
# invert predictions (back to original)
new_predicted = testPredict+test_shift.values.reshape(test_shift.size,1)
new_train_predicted= trainPredict+train_shift.values.reshape(train_shift.size,1)
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(new_train_predicted, Y_trainp))
#print('Train Score: %f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(new_predicted, Y_testp))
#print('Test Score: %f RMSE' % (testScore))
epochs = nb_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']
with open("output/%d_layers/compare.csv" % n_layers, "a") as fp:
fp.write("-MINIDOLAR/LSTM-Stateful NN\n")
hals = []
TRAIN_SIZE = 30
TARGET_TIME = 1
LAG_SIZE = 1
EMB_SIZE = 1
HIDDEN_RNN = 16
testScore_aux = 999999
f_aux = 0
for f in range(1,2):
name='relu'
model = Sequential()
#model.add(Dense(500, input_shape = (TRAIN_SIZE, )))
#model.add(Activation(name))
# model.add(LSTM(batch_size=batch_size,
# input_shape = (TRAIN_SIZE, EMB_SIZE,),
# units=HIDDEN_RNN, return_sequences=True, stateful=True, dropout=0.2, recurrent_dropout=0.2))
n_layers = n_layers+1 #para que o input 0 seja realmente uma camada, 1 serem 2, etc
for l in range(n_layers):
if(l==n_layers-1):
model.add(LSTM(batch_size=batch_size,
input_shape = (TRAIN_SIZE, EMB_SIZE,),
units=HIDDEN_RNN, return_sequences=False, stateful=True, dropout=0.2, recurrent_dropout=0.2))
else:
model.add(LSTM(batch_size=batch_size,
input_shape = (TRAIN_SIZE, EMB_SIZE,),
units=HIDDEN_RNN, return_sequences=True, stateful=True, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1))
model.add(Activation('linear'))
#model.summary()
trainScore, testScore, epochs, optimizer = evaluate_model(model, name, n_layers,nb_epoch)
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-1), "a") as fp:
fp.write("%i,%s,%f,%f,%d,%s --%s seconds\n" % (f, name, trainScore, testScore, epochs, optimizer, elapsed_time))
model = None
print("melhor parametro: %i" % f_aux)
if __name__ == "__main__":
__main__(sys.argv[1:])