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mlp_pib.py
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import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import sys
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
from keras.optimizers import SGD, Adam
from keras.callbacks import CSVLogger, EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau
from keras import regularizers
start_time = time.time()
# USD-BRL
dataframe = pandas.read_csv('pib_trimestral.csv', sep=',', engine='python', decimal='.', header=0)
dataset_original = dataframe['pib']
batch_size = 64
nb_epoch = 1000
patience = 1000
TARGET_TIME = 1
LAG_SIZE = 1
EMB_SIZE = 1
def evaluate_model(model, name, n_layers, ep, normalization, TRAIN_SIZE, dataset, ewm_dolar):
# X_train, X_test, Y_train, Y_test = dataset
# X_trainp, X_testp, Y_trainp, Y_testp = dadosp
if (normalization == 'AN'):
X_train, X_test, Y_train, Y_test, scaler, shift_train, shift_test, X_trainp, X_testp, Y_trainp, Y_testp = nn_an(
dataset, ewm_dolar, TRAIN_SIZE, TARGET_TIME, LAG_SIZE)
if (normalization == 'SW'):
X_train, X_test, Y_train, Y_test, scaler_train, scaler_test, X_trainp, X_testp, Y_trainp, Y_testp = 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)
# sgd = SGD(lr=0.64, momentum=0.8, nesterov=False)
# optimizer = sgd
# optimizer = Adam(lr=0.002)
optimizer = 'adam'
# optimizer = "adadelta"
# optimizer = "nadam"
model.compile(loss='mean_squared_error', optimizer=optimizer)
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()
# 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)
testPredict = model.predict(X_test)
# invert predictions (back to original)
if (normalization == 'AN'):
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_an_den(X_train, X_test, trainPredict, testPredict,
scaler, shift_train, shift_test)
if (normalization == 'SW'):
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_sw_den(X_train, X_test, trainPredict, testPredict,
scaler_train, scaler_test)
if (normalization == 'MM'):
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_mm_den(X_train, X_test, trainPredict, testPredict,
scaler)
if (normalization == 'ZS'):
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_zs_den(X_train, X_test, trainPredict, testPredict,
scaler)
if (normalization == 'DS'):
X_trainp3, X_testp3, new_train_predicted, new_predicted = nn_ds_den(X_train, X_test, trainPredict, testPredict,
maximum)
# np.savetxt("output/previsto.csv", new_predicted)
# np.savetxt("output/real.csv", Y_testp)
# np.savetxt("output/previsto_treino.csv", new_train_predicted)
# np.savetxt("output/real_treino.csv", Y_trainp)
# np.savetxt("output/x_test-meu.csv", X_testp)
# np.savetxt("output/y_test-meu.csv", Y_testp)
# np.savetxt("output/x_treino-meu.csv", X_trainp)
# np.savetxt("output/y_treino-meu.csv", Y_trainp)
# 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 = 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("-PIB/MLP NN\n")
hals = []
# best parameters without outlier removal: TRAIN_SIZE= 7 k=25
# with outlier removal: TRAIN_SIZE=4 k=3
for o in range(2, 16):
for p in range(2, 30):
TRAIN_SIZE = o
k = p
ewm_dolar = dataset_original.ewm(span=k, min_periods=k).mean()
# removendo NaN
dataset = np.array(dataset_original.iloc[k - 1:])
ewm_dolar = np.array(ewm_dolar.iloc[k - 1:])
# for name in nonlinearities:
for normalization in normalizations:
# for f in range(1,2):
name = 'tanh'
model = Sequential()
model.add(Dense(12, input_shape=(TRAIN_SIZE,), kernel_initializer='glorot_uniform',
kernel_regularizer=regularizers.l2(0.01)))
model.add(Activation(name))
model.add(Dropout(0.25))
for l in range(n_layers):
model.add(Dense(12, input_shape=(TRAIN_SIZE,)))
model.add(Activation(name))
# model.add(Dropout(0.25))
model.add(Dense(1))
model.add(Activation(name))
# model.summary()
trainScore, testScore, epochs, optimizer = evaluate_model(model, name, n_layers, nb_epoch,
normalization, TRAIN_SIZE, dataset, ewm_dolar)
# 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("w=%i,k=%i,%s,%s,%f,%f,%d,%s --%s seconds\n" % (
o, p, name, normalization, trainScore, testScore, epochs, optimizer, elapsed_time))
model = None
if __name__ == "__main__":
__main__(sys.argv[1:])