from sklearn.grid_search import GridSearchCV
from data.TrafficFeeder import TrafficFeeder
from experiments.initializer import *
from utils.SplitTrainTest import SplitTrainTest
param_dicts = {
"Q": np.arange(0.01, 0.1, step=0.01),
"epsilon": np.arange(0.1, 1.0, step=0.05)
}
n_sliding_window = 4
n_periodic = 1
n_input = n_sliding_window + n_periodic
neural_shape = [n_input, 15, 1]
dataFeeder = TrafficFeeder()
X, y = dataFeeder.fetch_traffic_training(n_sliding_window, n_periodic,
(40, 46))
estimator = ACOEstimator()
archive_solution = construct_solution(estimator.number_of_solutions,
neural_shape)
cv = SplitTrainTest(X.shape[0])
fit_param = {'neural_shape': neural_shape, "archive": archive_solution}
# estimator.fit(X,y,**fit_param)
gs = GridSearchCV(estimator,
param_grid=param_dicts,
cv=cv,
n_jobs=-1,
fit_params=fit_param,
scoring='mean_squared_error')
gs.fit(X, y)
print gs.best_estimator_
print gs.best_estimator_.score(X, y)
plt.show()
if __name__ == '__main__':
best_estimator = None
best_score = np.Inf
for loop in np.arange(1, 20):
n_input = 4
n_periodic = 1
n_hidden = 15
neural_shape = [n_input + n_periodic, n_hidden, 1]
Q = 0.09
epsilon = 0.55
dataFeeder = TrafficFeeder()
X_train, y_train = dataFeeder.fetch_traffic_training(
n_input, 1, (40, 46))
X_test, y_test = dataFeeder.fetch_traffic_test(n_input, 1, (46, 48))
# retrieve = [n_input+1,(X_train,y_train,X_test,y_test)]
acoNet = ACOEstimator(Q=Q, epsilon=epsilon)
fit_param = {"neural_shape": neural_shape}
acoNet.fit(X_train, y_train, **fit_param)
fit_param["weights_matrix"] = acoNet.best_archive
neuralNet = NeuralFlowRegressor()
neuralNet.fit(X_train, y_train, **fit_param)
y_pred = dataFeeder.convert(neuralNet.predict(X_test))
score = np.sqrt(mean_squared_error(y_pred, y_test))
if (score < best_score):
best_estimator = acoNet
print score
# plot_fig(y_pred,y_test)
print best_score, best_estimator
import numpy as np
if __name__ == '__main__':
np.random.seed(7)
n_input = 4
n_periodic = 0
n_hidden = 15
neural_shape = [n_input + n_periodic, n_hidden, 1]
cross_rate = 0.6
mutation_rate = 0.04
pop_size = 50
dataFeeder = TrafficFeeder()
X_train, y_train = dataFeeder.fetch_traffic_training(
n_input, n_periodic, (40, 47))
X_test, y_test = dataFeeder.fetch_traffic_test(n_input, n_periodic,
(48, 50))
# retrieve = [n_input+1,(X_train,y_train,X_test,y_test)]
gaEstimator = GAEstimator(cross_rate=cross_rate,
mutation_rate=mutation_rate,
pop_size=pop_size)
fit_param = {"neural_shape": neural_shape}
gaEstimator.fit(X_train, y_train, **fit_param)
fit_param["weights_matrix"] = gaEstimator.best_archive
neuralNet = NeuralFlowRegressor()
neuralNet.fit(X_train, y_train, **fit_param)
y_pred = dataFeeder.convert(neuralNet.predict(X_test))
print "done"
print sqrt(mean_squared_error(y_pred, y_test))
plot_figure(y_pred, y_test)