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
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>
#
# # param_aco = {
