def model_fit(param):
print "Training %s" % param[0]
neural_shape = [param[0], 15, 1]
X_train = param[1][0]
y_train = param[1][1]
X_test = param[1][2]
y_test = param[1][3]
fit_param = {"neural_shape": neural_shape}
neuralNet = NeuralFlowRegressor()
neuralNet.fit(X_train, y_train, **fit_param)
return param[0], np.sqrt(neuralNet.score(X_test, y_test))
def model_fit(param):
print "Training %s"%param[0]
neural_shape = [param[0],15,1]
X_train = param[1][0]
y_train = param[1][1]
X_test = param[1][2]
y_test = param[1][3]
fit_param = {
"neural_shape":neural_shape
}
neuralNet = NeuralFlowRegressor()
neuralNet.fit(X_train,y_train,**fit_param)
return param[0],np.sqrt(neuralNet.score(X_test,y_test))
def model_fit(param):
print "Training %s" % param[0]
neural_shape = [param[0], 15, 1]
X_train = param[1][0]
y_train = param[1][1]
X_test = param[1][2]
y_test = param[1][3]
acoNet = ACOEstimator(Q=0.08, epsilon=0.55)
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)
return param[0], np.sqrt(neuralNet.score(X_test, y_test))
def model_fit(param):
print "Training %s" % param[0]
X_train = param[1][0]
y_train = param[1][1]
X_test = param[1][2]
y_test = param[1][3]
neural_shape = [y_train.shape[1] * param[0], 10, y_train.shape[1]]
acoNet = GAEstimator(cross_rate=0.65, mutation_rate=0.01, pop_size=50)
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)
return param[0], np.sqrt(neuralNet.score(X_test, y_test))
def model_fit(param):
print "Training %s"%param[0]
X_train = param[1][0]
y_train = param[1][1]
X_test = param[1][2]
y_test = param[1][3]
neural_shape = [y_train.shape[1]*param[0],10,y_train.shape[1]]
acoNet = ACOEstimator(Q=0.08,epsilon=0.55)
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)
return param[0],np.sqrt(neuralNet.score(X_test,y_test))
def model_fit(param):
print "Training %s"%param[0]
neural_shape = [param[0],15,1]
X_train = param[1][0]
y_train = param[1][1]
X_test = param[1][2]
y_test = param[1][3]
acoNet = GAEstimator(cross_rate=0.65,mutation_rate=0.01,pop_size=50)
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)
return param[0],np.sqrt(neuralNet.score(X_test,y_test))
def estimator(n_windows, n_hidden_nodes):
data = data_train[n_windows]
X_train = data[0]
y_train = data[1]
X_test = data[2]
y_test = data[3]
fit_param = {'neural_shape': [2 * n_windows, n_hidden_nodes, 2]}
neuralNet = NeuralFlowRegressor()
kfold = KFold(X_train.shape[0], 5)
score_lst = np.zeros(len(kfold), dtype=np.float32)
for k, (train, test) in enumerate(kfold):
neuralNet.fit(X_train[train], y_train[train], **fit_param)
nn_shape = "%s-%s" % (2 * n_windows, n_hidden_nodes)
score = neuralNet.score(X_test, y_test)
neuralNet.save("tmp/score_%s" % score)
return nn_shape, score
def estimator(n_windows,n_hidden_nodes):
data = data_train[n_windows]
X_train = data[0]
y_train = data[1]
X_test = data[2]
y_test = data[3]
fit_param = {
'neural_shape':[2*n_windows,n_hidden_nodes,2]
}
neuralNet = NeuralFlowRegressor()
kfold = KFold(X_train.shape[0],5)
score_lst = np.zeros(len(kfold),dtype=np.float32)
for k,(train,test) in enumerate(kfold):
neuralNet.fit(X_train[train],y_train[train],**fit_param)
nn_shape = "%s-%s"%(2*n_windows,n_hidden_nodes)
score = neuralNet.score(X_test,y_test)
neuralNet.save("tmp/score_%s"%score)
return nn_shape,score
X_test = X_dat[training_set_size:training_set_size+testing_set_size]
y_train = fuzzy_train[1:training_set_size+1]
y_test = data[training_set_size+1:training_set_size+testing_set_size+1]
#
# # Setup paramters of model
# n_hidden = np.array([80])
# # neural_shape = [X_train.shape[1],n_hidden,y_train.shape[1]]
# # Initialize ACO Estimator
# estimator = ACOEstimator(Q=0.65,epsilon=0.1,number_of_solutions=130,hidden_nodes=n_hidden)
# # estimator = GAEstimator()
# # fit_param = {'neural_shape':neural_shape}
score_nodes = {}
print "Initialize estimator"
for n_hidden in np.arange(28,80):
neuralNet = NeuralFlowRegressor(learning_rate=1E-03,hidden_nodes=np.array([n_hidden]))
neuralNet.fit(X_train,y_train)
print "Score hidden nodes = %s : %s"%(n_hidden,neuralNet.score(X_train,y_train))
score_nodes["%s"%n_hidden]=neuralNet.score(X_train,y_train)
print score_nodes
score_df = pd.DataFrame(score_nodes.items(),columns=['Nodes','Score'])
score_df.to_csv('score_lst.csv',index=None)
#gridSearch = GridSearchCV(neuralNet,{'hidden_nodes':[np.arange(28,50).reshape(-1,1)]},n_jobs=-1)
#gridSearch.fit(X_train,y_train)
# print gridSearch.score(X_test,y_test)
#print gridSearch.grid_scores_
#print "Best model %s"%gridSearch.best_estimator_
# optimizer = OptimizerNNEstimator(estimator,neuralNet)
# optimizer.fit(X_train,y_train)
#
# # <codecell>
#
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 pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from utils.SlidingWindowUtil import SlidingWindow
from io_utils.GFeeder import GFeeder
from estimators.NeuralFlow import NeuralFlowRegressor
from utils.GraphUtil import *
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
dat = pd.read_csv('../sample_610_10min.csv', index_col=0, parse_dates=True)
training_size = 3000
test_size = 600
gFeeder = GFeeder()
dat.cpu_rate = np.array(scaler.fit_transform(dat.cpu_rate))
X_dat = np.array(list(SlidingWindow(dat.cpu_rate, sliding_number=4)))
X_train = X_dat[:training_size]
y_train = np.array(dat.cpu_rate[:training_size].tolist()).reshape(-1, 1)
X_test = X_dat[training_size + 1:training_size + 1 + test_size]
y_test = np.array(dat.cpu_rate[training_size + 1:training_size + test_size +
1].tolist()).reshape(-1, 1)
nn = NeuralFlowRegressor(learning_rate=1E-03, hidden_nodes=np.array([55]))
nn.fit(X_train, y_train)
y_pred = nn.predict(X_test)
plot_figure(y_pred=y_pred, y_true=y_test, title="Neural Flow sliding window 4")
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from utils.SlidingWindowUtil import SlidingWindow
from io_utils.GFeeder import GFeeder
from estimators.NeuralFlow import NeuralFlowRegressor
from utils.GraphUtil import *
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
dat = pd.read_csv('../sample_610_10min.csv',index_col=0,parse_dates=True)
training_size = 3000
test_size = 600
gFeeder = GFeeder()
dat.cpu_rate = np.array(scaler.fit_transform(dat.cpu_rate))
X_dat = np.array(list(SlidingWindow(dat.cpu_rate,sliding_number=4)))
X_train = X_dat[:training_size]
y_train = np.array(dat.cpu_rate[:training_size].tolist()).reshape(-1,1)
X_test = X_dat[training_size + 1:training_size + 1 + test_size]
y_test = np.array(dat.cpu_rate[training_size + 1:training_size + test_size + 1].tolist()).reshape(-1,1)
nn = NeuralFlowRegressor(learning_rate=1E-03,hidden_nodes=np.array([55]))
nn.fit(X_train,y_train)
y_pred = nn.predict(X_test)
plot_figure(y_pred=y_pred,y_true=y_test,title="Neural Flow sliding window 4")
from estimators.NeuralFlow import NeuralFlowRegressor
from utils.GraphUtil import *
if __name__ == '__main__':
n_input = 4
n_periodic = 1
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,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)]
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 sqrt(mean_squared_error(y_pred,y_test))
plot_figure(y_pred,y_test)