def run_neural_net(connection, data):
#import_modules()
dataset = data
data, target = dataset.data, dataset.target
data_scalar = preprocessing.MinMaxScaler()
target_scalar = preprocessing.MinMaxScaler()
data = data_scalar.fit_transform(data)
target = target_scalar.fit_transform(target.reshape(-1, 1))
environment.reproducible()
x_train, x_test, y_train, y_test = train_test_split(data,
target,
train_size=0.85)
cgnet = algorithms.ConjugateGradient(
connection,
search_method='golden',
show_epoch=5,
verbose=True,
addons=[algorithms.LinearSearch],
)
time_start = time.time()
cgnet.train(x_train, y_train, x_test, y_test, epochs=50)
time_end = time.time()
#plots.error_plot(cgnet)
y_predict = cgnet.predict(x_test).round(1)
error = mae(target_scalar.inverse_transform(y_test), \
target_scalar.inverse_transform(y_predict))
#print(time_end - time_start)
#print(target_scalar.inverse_transform(y_test), \
# target_scalar.inverse_transform(y_predict))
#print(error)
return ([time_end - time_start, error])
# for std_in in np.array([0.01, 0.1, 0.5, 0.8, 1.0, 1.2, 1.4, 2.0, 5.0, 10.0]):
# grnnet = algorithms.GRNN(std=std_in, verbose=True)
# grnnet.train(training_X, training_Y)
# predicted = grnnet.predict(testing_X)
# error = scorer(testing_Y, predicted)
# print("GRNN RMSE = {:.5f}\n".format(error))
# if error < best_error:
# print("New Best Error Found: " + str(error))
# best_error = error
# best_model = grnnet
grnnet2 = algorithms.GRNN(std=0.2, verbose=True)
grnnet2.train(training_X, training_Y)
y_predicted = grnnet2.predict(testing_X)
print("RMSE = " + str(estimators.rmse(y_predicted, testing_Y.ravel())))
print("MAE = " + str(estimators.mae(y_predicted, testing_Y.ravel())))
actual_mae = y_data_scaler.inverse_transform(
estimators.mae(y_predicted, testing_Y))
print("MAE (no. of shares) = " + str(actual_mae.squeeze()))
# Save the best GRNN model
import _pickle
with open(
'/home/pier/Machine_Learning/KE5206NN/regression/regression_models/grnn.pkl',
'wb') as fid:
_pickle.dump(grnnet2, fid)
# GRNN Best values
#
# RMSE = 0.019625235702837703
# MAE = 0.004645349837893423
def test_mae(self):
predicted = np.array([1, 2, 3])
target = np.array([3, 2, 1])
actual = estimators.mae(target, predicted)
self.assertAlmostEqual(actual, 4 / 3.)
def test_mae(self):
predicted = np.array([1, 2, 3])
target = np.array([3, 2, 1])
actual = estimators.mae(target, predicted)
self.assertAlmostEqual(actual, 4 / 3.)
from neupy import estimators
import _pickle
with open(
'/Users/pierlim/PycharmProjects/KE5206NN/regression/regression_models/multi_layer_perceptron.pkl',
'rb') as fid:
mlp = _pickle.load(fid)
with open(
'/Users/pierlim/PycharmProjects/KE5206NN/regression/regression_models/grnn.pkl',
'rb') as fid:
grnn = _pickle.load(fid)
y_mlp_predicted = mlp.predict(testing_X)
print("MLP RMSE = " + str(estimators.rmse(y_mlp_predicted, testing_Y.ravel())))
mlp_mae = estimators.mae(y_mlp_predicted, testing_Y.ravel())
print("MLP MAE = " + str(estimators.mae(y_mlp_predicted, testing_Y.ravel())))
actual_mae = y_data_scaler.inverse_transform(
estimators.mae(y_mlp_predicted, testing_Y))
print("MLP MAE (no. of shares) = " + str(actual_mae.squeeze()))
y_grnn_predicted = grnn.predict(testing_X)
y_grnn_predicted = y_grnn_predicted.ravel().transpose()
grnn_mae = estimators.mae(y_grnn_predicted, testing_Y.ravel())
print("GRNN RMSE = " +
str(estimators.rmse(y_grnn_predicted, testing_Y.ravel())))
print("GRNN MAE = " + str(estimators.mae(y_grnn_predicted, testing_Y.ravel())))
actual_mae = y_data_scaler.inverse_transform(
estimators.mae(y_grnn_predicted, testing_Y))
print("GRNN MAE (no. of shares) = " + str(actual_mae.squeeze()))
print(y_grnn_predicted.shape)
#
# training_X = x_data_scaler.transform(training_X)
# training_Y = y_data_scaler.transform(training_Y.reshape(-1, 1))
#
# testing_X = x_data_scaler.transform(testing_X)
# testing_Y = y_data_scaler.transform(testing_Y.reshape(-1, 1))
scaler = preprocessing.StandardScaler()
for n in range(training_X.shape[1]):
training_X[n] = scaler.fit_transform(training_X[n].reshape(-1, 1))
testing_X[n] = scaler.fit_transform(testing_X[n].reshape(-1, 1))
training_Y = scaler.fit_transform(training_X[' shares'].reshape(-1, 1))
testing_Y = scaler.fit_transform(testing_X[' shares'].reshape(-1, 1))
# MLPP Scikit-Learn - RMSE 0.01567453951930472
from sklearn.neural_network import MLPRegressor
from scipy import stats
from sklearn.grid_search import RandomizedSearchCV
from neupy import estimators
mlp = MLPRegressor(hidden_layer_sizes=(10, 10, 10), max_iter=2000, activation='tanh', solver='sgd',
learning_rate='constant', early_stopping=True, learning_rate_init=0.04, alpha=100, beta_1=0.616,
beta_2=0.194)
mlp.fit(training_X, training_Y)
y_predicted = mlp.predict(testing_X)
print("RMSE = " + str(estimators.rmse(y_predicted, testing_Y.ravel())))
print("MAE = " + str(estimators.mae(y_predicted, testing_Y.ravel())))
actual_mae = y_data_scaler.inverse_transform(estimators.mae(y_predicted, testing_Y))
print("MAE (no. of shares) = " + str(actual_mae.squeeze()))