def train_model(self):
if self.verbose:
print("training GP model ...")
self.gpMdl = gp.GPR()
m = gp.mean.Zero()
k = gp.cov.RBFard(D=None,
log_ell_list=self.gp_hyp[:-1],
log_sigma=self.gp_hyp[-1])
'''
try:
self.gpMdl.setPrior(mean=m, kernel=k)
self.gpMdl.setNoise(log_sigma = np.log(0.8))
self.gpMdl.setOptimizer('BFGS')#('minimize');
#self.gpMdl.getPosterior(self.gprX, self.gprY)
self.gpMdl.optimize(self.gprX,self.gprY)#,numIters=100)
except:
print('cannot quasi-newton it') '''
#self.gpMdl = gp.GPR()
self.gpMdl.setPrior(mean=m, kernel=k)
self.gpMdl.setNoise(log_sigma=np.log(0.7))
#self.gpMdl.getPosterior(self.gprX,self.gprY)
self.gpMdl.setOptimizer('Minimize')
self.gpMdl.optimize(self.gprX, self.gprY,
numIterations=10) #,numIters=100)
return self.gpMdl.covfunc.hyp
def compute_gp_regression(X_train, y_train, X_test):
model = pyGPs.GPR()
m = pyGPs.mean.Const(0)
k = pyGPs.cov.RBF()
model.setPrior(mean=m, kernel=k)
model.optimize(X_train, y_train)
y_pred, _, _, _, _ = model.predict(X_test)
return y_pred
def test_GPR(self):
print("testing GP regression...")
model = pyGPs.GPR()
m = pyGPs.mean.Zero()
k = pyGPs.cov.RBF()
model.setPrior(mean=m, kernel=k)
model.setOptimizer("Minimize", num_restarts=10)
model.optimize(self.xr, self.yr)
model.predict(self.zr)
self.checkRegressionOutput(model)
def run():
model = pyGPs.GPR()
x, y, z = generate_toy_data()
lel = np.apply_along_axis(np.std, 0, x)
print "parameters"
print lel
lengthscale = edistance_at_percentile(x, 50)
print lengthscale
# TODO: set non-default parameters
k = pyGPs.cov.RBFard(log_ell_list=[0.01, 0.01],
log_sigma=0.01) #D=x.shape[1])
m = pyGPs.mean.Const()
model.setPrior(mean=m, kernel=k)
print "hyperparameters"
print k.hyp
model.optimize(x, y)
print "posterior", model.posterior
print "Negative log marginal liklihood optimized:", round(model.nlZ, 3)
def objective(x):
ymu, ys2, fmu, fs2, lp = model.predict(x.reshape((1, len(x))))
ret = ymu - 1.645 * np.sqrt(ys2)
return ret[0][0]
x_opt = fmin_bfgs(lambda x: objective(x) * -1, np.arange(0, 0.2, 0.1))
print "Optimized value of x:", x_opt
ymu, ys2, fmu, fs2, lp = model.predict(z)
q_95 = ymu + 1.645 * np.sqrt(ys2)
q_5 = ymu - 1.645 * np.sqrt(ys2)
t1 = sort_for_plotting(z[:, -1].reshape(len(z), 1), q_95, q_5)
t2 = sort_for_plotting(z[:, 0].reshape(len(z), 1), q_95, q_5)
plt.figure()
ymu = np.reshape(ymu, (ymu.shape[0], ))
plt.plot(z[:, -1], ymu, ls='None', marker='+')
plt.fill_between(t1[:, 0],
t1[:, 1],
t1[:, 2],
facecolor=[0.7539, 0.89453125, 0.62890625, 1.0],
linewidths=0)
plt.show()
plt.figure()
plt.plot(z[:, 0], ymu, ls='None', marker='+')
plt.fill_between(t2[:, 0],
t2[:, 1],
t2[:, 2],
facecolor=[0.7539, 0.89453125, 0.62890625, 1.0],
linewidths=0)
plt.show()
def setUp(self):
# fix random seed
np.random.seed(0)
# random data for testing
n = 20 # number of inputs
D = 3 # dimension of inputs
self.x = np.random.normal(loc=0.0, scale=1.0, size=(n, D))
self.y = np.random.random((n, ))
self.model = pyGPs.GPR()
nlZ, dnlZ, post = self.model.getPosterior(self.x, self.y)
self.nlZ_beforeOpt = nlZ
def optimize_max_possible_value(x, y, grid, func):
model = pyGPs.GPR()
np_x = np.array(x)
np_y = np.array(y)
np_z = np.array(z)
model.getPosterior(np_x, np_y)
model.optimize(np_x, np_y)
used_points = set()
for step in xrange(100):
l = model.predict(np_z)
possible_max_point = None
possible_max_value = None
possible_max_index = None
N = 2
for i in xrange(len(z)):
point = z[i]
value = l[0][i][0]
variance = sqrt(l[1][i][0])
if possible_max_value is None or possible_max_value < value + variance * N:
possible_max_point = point
possible_max_value = value + variance * N
possible_max_index = i
print possible_max_index, possible_max_point, possible_max_value
if possible_max_index in used_points:
return possible_max_point, func(possible_max_point)
used_points.add(possible_max_index)
x.append(possible_max_point)
y.append(func(possible_max_point))
np_x = np.array(x)
np_y = np.array(y)
np_z = np.array(z)
model = pyGPs.GPR()
model.getPosterior(np_x, np_y)
model.optimize(np_x, np_y)
return possible_max_point, func(possible_max_point)
def test(self, features, version, label):
"""
Learns GPR and KNR model from given training set and test on test set.
Here, training set consists of every odd feature and test set consists of every training set is equal to test set.
:param features:
:param version:
:param label:
:return:
"""
groundtruth = np.load(self.param_path + '/v' + str(version) + '_' +
self.GT)
_trainX = np.concatenate(features[0:features.shape[0]:2])
_trainY = np.concatenate(groundtruth[0:groundtruth.size:2])
testX = features[1:features.shape[0]:2]
testY = groundtruth[1:groundtruth.size:2]
print 'features.shape: ', features.shape, ', groundtruth.shape: ', groundtruth.shape
print '_trainX.shape: ', _trainX.shape, ', _trainY.shape: ', _trainY.shape
trainX, trainY = self.exclude_label(_trainX, _trainY, c=0)
PYGPR = 'gpr_' + label
KNR = 'knr_' + label
if files.isExist(self.model_path, PYGPR):
gprmodel = self.loadf(self.model_path, PYGPR)
knrmodel = self.loadf(self.model_path, KNR)
else:
print 'Learning GPR model'
gprmodel = pyGPs.GPR()
gprmodel.getPosterior(trainX, trainY)
gprmodel.optimize(trainX, trainY)
self.savef(self.model_path, PYGPR, gprmodel)
print 'Learning KNR model'
knrmodel = knr(trainX, trainY)
self.savef(self.model_path, KNR, knrmodel)
print 'Learning both GPR and KNR model is DONE.'
self.plot_gpr(gprmodel, testX, testY, label, 'odd_feature')
self.plot_knr(knrmodel, testX, testY, label, 'odd_feature')
def calculateRMSEPyGP(vectorX,vectorY,labelList):
"""
calculate the root mean squared error
Parameters:
-----------
vectorX: timestamps of the timeseries
vectorY: valueSet of the timeseries
labelList: labels of the timeseries
Returns:
--------
list of (household,rmse) tuples
"""
#setX = [preprocessing.scale(element )for element in vectorX]
setY=preprocessing.scale(vectorY,axis=1)
model = pyGPs.GPR() # specify model (GP regression)
k = pyGPs.cov.Linear() + pyGPs.cov.RBF() #hyperparams will be set with optimizeHyperparameters method
model.setPrior(kernel=k)
hyperparams, model2 = GPE.optimizeHyperparameters([0.0000001,0.0000001,0.0000001],model,vectorX,setY,bounds=[(None,5),(None,5),(None,5)],method = 'L-BFGS-B')
print('hyerparameters used:',hyperparams)
y_pred, ys2, fm, fs2, lp = model2.predict(vectorX[0])
#plot general model after normalizing the input timeseries
plt.plot(y_pred, color='red')
for i in setY:
plt.plot(i,color='blue')
plt.show(block=True)
rmseData = []
for i in range(0,len(vectorY),1):
rmse = mean_squared_error(vectorY[i], y_pred)**0.5
HH = labelList[i]
rmseData.append((HH,rmse))
return rmseData
def test_trainset_test_same(self, features, version, label):
"""
Learns GPR and KNR model from given training set and test on test set.
Here, training set is equal to test set.
:param features:
:param version:
:param label:
:return:
"""
groundtruth = np.load(self.param_path + '/v' + str(version) + '_' +
self.GT)
_trainX = np.concatenate(features)
_trainY = np.concatenate(groundtruth)
trainX, trainY = self.exclude_label(_trainX, _trainY, c=0)
testX = features
testY = groundtruth
PYGPR = 'gpr_all_' + label
KNR = 'knr_all_' + label
if files.isExist(self.model_path, PYGPR):
gprmodel = self.loadf(self.model_path, PYGPR)
knrmodel = self.loadf(self.model_path, KNR)
else:
print 'Learning GPR model'
gprmodel = pyGPs.GPR()
gprmodel.getPosterior(trainX, trainY)
gprmodel.optimize(trainX, trainY)
self.savef(self.model_path, PYGPR, gprmodel)
print 'Learning KNR model'
knrmodel = knr(trainX, trainY)
self.savef(self.model_path, KNR, knrmodel)
print 'Learning both GPR and KNR model is DONE.'
self.plot_gpr(gprmodel, testX, testY, label, 'all_feature')
self.plot_knr(knrmodel, testX, testY, label, 'all_feature')
def InitModel(self):
# initialize search space
self.x = np.array([[sum(n)/2 for n in self.domain]])
self.y = np.array([self.func(self.x[0])])
self.regret = np.array([np.linalg.norm(self.x[0] - self.optima)])
self.regretBound = np.array([1])
self.covF = np.array([0])
self.covTr = np.array([0])
# specify model (GP regression)
self.model = pyGPs.GPR()
m = pyGPs.mean.Zero()
k = pyGPs.cov.Linear()
if model.kernel == 'RBF':
k = pyGPs.cov.RBF()
if model.kernel == 'Matern':
k = pyGPs.cov.Matern()
self.model.setPrior(mean=m, kernel=k)
def gpr(p, *args):
p1 = p[0]
p2 = p[1]
p3 = p[2]
# x, y, xs, ys = args
# min_max_scaler1 = preprocessing.MinMaxScaler()
# min_max_scaler2 = preprocessing.MinMaxScaler()
# min_max_scaler3 = preprocessing.MinMaxScaler()
# min_max_scaler4 = preprocessing.MinMaxScaler()
# x = min_max_scaler1.fit_transform(x)
# xs = min_max_scaler2.fit_transform(xs)
# y = min_max_scaler3.fit_transform(y)
# ys = min_max_scaler4.fit_transform(ys)
x, y, ys = args
min_max_scaler1 = preprocessing.MinMaxScaler()
min_max_scaler2 = preprocessing.MinMaxScaler()
min_max_scaler3 = preprocessing.MinMaxScaler()
x = min_max_scaler1.fit_transform(x)
y = min_max_scaler2.fit_transform(y)
ys = min_max_scaler3.fit_transform(ys)
pca = PCA(n_components='mle')
x_pca = pca.fit_transform(x)
k1 = pyGPs.cov.RBF(np.log(p1), np.log(p2)) + pyGPs.cov.Noise(np.log(p3))
# STANDARD GP (prediction)
m = pyGPs.mean.Linear(D=x_pca[0:600, :].shape[1]) + pyGPs.mean.Const()
model = pyGPs.GPR()
model.setData(x_pca[0:600, :], y)
model.setPrior(mean=m, kernel=k1)
# STANDARD GP (training)
# model.optimize(x, y)
ymu, ys2, fmu, fs2, lp = model.predict(x_pca[600:660, :])
ymu = min_max_scaler3.inverse_transform(ymu)
ys = min_max_scaler3.inverse_transform(ys)
rmse = pyGPs.Validation.valid.RMSE(ymu, ys)
return rmse
def __init__(self, input, n_in, n_out, output):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
input = np.asarray(input)
self.y = np.asarray(output)
model = pyGPs.GPR()
model.setData(input, self.y)
model.optimize()
ymu, ys2, fmu, fs2, lp = model.predict(input)
self.lp = lp
self.ymu = ymu
def __init__(self, winSize = 500, gpWinSize = 40,gpin_winSize =40 ,nu_cluster = 3,
controlFeedback = 1, alpha = 7, beta = 2, sigma = 0.9, controllerDelay = 6 ):
# online Learning parameters and variables
self.__winSize = winSize
self.__gpWinSize = gpWinSize
self.__gpin_winSize = gpin_winSize ;
self.__fileName = "data_init.csv"
# system data
self.__inData = []
self.__outData = []
self.__statData = []
self.__clusData = []
# unclontroled input forecast model
self.__gpfX = []
self.__gpfY = []
self.__sysState = []
# Number of clusters
self.__Nc = nu_cluster
self.__onlineCluster = 1
# model status
self.__modelInitilization = 0 # 0: initial, 1: learn and predict
self.__modelTimeIdx = 0
self.__modelstate = 0
# model control output
self.__controlOut = []
self.__controlFeedback = controlFeedback
self.__alpha = alpha
self.__beta = beta
self.__sigma = sigma
self.__controllerDelay = controllerDelay
self.__controllerCounter = 0
# model objects
# state models
self.__GPs = []
# forecast model
self.__gpFmdl = gp.GPR()
# classifier model
self.__myCluster = KMeans(n_clusters=self.__Nc ,init='random', random_state=0)
# variables to store prediction data for evaluation and plotting:
# forecasting workload variables
self.__In_mean = []
self.__In_si = []
self.__In_pred_data = []
# classification variables
self.__sysModes = []
# for output variables
self.__out_mean =[]
self.__out_si = []
self.__out_pred_data = []
# for i in self.__Nc:
# self.__out_mean.append([])
# self.__out_si.append([])
# self.__out_pred_data.append([])
# other
self.verbose = 1
# pass the offline data to initialize the model
# input, output and model state indices in data
indxIn = 0 # Input indices
indxOut = 18 # Output indices
indxS = [14,17] # State indices
indxQ = [2,3, 4, 5, 6, 8, 12, 13] #clustering data indicies
inData = 0
outData = 0
statData = []
clusData = [];
with open(self.__fileName, 'rt') as dataFile:
reader = csv.reader(dataFile, delimiter=',')
for row in reader:
inData = float(row[indxIn])
outData = float(row[indxOut])
statData = [float(row[i]) for i in indxS]
clusData = [float(row[i]) for i in indxQ]
self.__initModel(inData, statData, outData, clusData)
if self.__modelInitilization:
break
def __initModel(self, sysIn, sysStat, sysOut, sysClus):
# buffer data untill the window size is reached
self.__inData.append(sysIn)
self.__outData.append(sysOut)
self.__statData.append(sysStat)
self.__clusData.append(sysClus)
# Lean forecast model and system indx
self.__gpfX.append(self.__modelTimeIdx)
self.__gpfY.append(sysIn)
while len(self.__gpfX) > self.__gpin_winSize:
# delete oldest data
self.__gpfX.pop(0)
self.__gpfY.pop(0)
self.__modelTimeIdx += 1
if len(self.__inData) >= self.__winSize:
# Learn the models (align the data to input output format Y(k+1) = f(x(k),u(k+1)))
self.__inData.pop(0)
self.__outData.pop(0)
# self.__statData.pop()
# self.__clusterFeatures.pop()
# classify the data
clustersX = self.__myCluster.fit_predict(self.__clusData)
if self.verbose:
print ("training state-space models using GPs")
for i in range(self.__Nc):
gprX = []
gprY = []
for j in range(len(clustersX) - 1):
if clustersX[j] == i:
gprX.append([self.__inData[j]] + self.__statData[j])
gprY.append(self.__outData[j])
gprX = np.array(gprX)
gprY = np.array(gprY)
gpmdl = gp.GPR()
m = gp.mean.Zero()
RBF_hyp_init = [0.5] * (len(sysStat) + 2)
k = gp.cov.RBFard(D=None, log_ell_list=RBF_hyp_init[:-1], log_sigma=RBF_hyp_init[-1])
gpmdl.setPrior(mean=m, kernel=k)
if self.verbose:
print ("training GP of mode: " + str(i))
# gpmdl.getPosterior(gprX,gprY)
gpmdl.setNoise(log_sigma=np.log(0.8))
gpmdl.setOptimizer('Minimize') # ('Minimize');
gpmdl.optimize(gprX, gprY) # ,numIterations=100)
self.__GPs.append(gpmdl)
if self.verbose:
print ("training forecast Model using GP")
try:
k_f = gp.cov.RBF(log_ell=1, log_sigma=1)
self.__gpFmdl.setPrior(mean=gp.mean.Zero(), kernel=k_f)
self.__gpFmdl.setNoise(log_sigma=np.log(0.8))
self.__gpFmdl.setOptimizer('BFGS') # ('Minimize');
self.__gpFmdl.optimize(np.array(self.__gpfX), np.array(self.__gpfY)) # ,numIterations=100)
except:
print('can quasi-newton it (forecast)')
self.__gpFmdl = gp.GPR()
k_f = gp.cov.RBF(log_ell=1, log_sigma=1)
self.__gpFmdl.setPrior(mean=gp.mean.Zero(), kernel=k_f)
self.__gpFmdl.setNoise(log_sigma=np.log(0.8))
self.__gpFmdl.setPrior(mean=gp.mean.Zero(), kernel=k_f)
self.__gpFmdl.setOptimizer('Minimize') # ('Minimize');
self.__gpFmdl.optimize(np.array(self.__gpfX), np.array(self.__gpfY)) # , numIterations=100)
self.__sysState = self.__statData[-1]
self.__modelInitilization = 1
def __initModel(self, sysIn, sysStat, sysOut):
self.__inData.append(sysIn)
self.__outData.append(sysOut)
self.__statData.append(sysStat)
self.__featurewin.append(sysStat)
while len(self.__featurewin) < self.__featureWinSize:
self.__featurewin.append(sysStat)
while len(self.__featurewin) > self.__featureWinSize:
self.__featurewin.pop(0)
curr_feature = self.__featureExtraction(self.__featurewin)
# update feature data (slid the window)
self.__clusterFeatures.append(curr_feature)
# update forecast model and system indx
# add the new data (slid the win)
self.__gpfX.append(self.__modelTimeIdx)
self.__gpfY.append(sysIn)
while len(self.__gpfX) > self.__gpWinSize:
# delete oldest data
self.__gpfX.pop(0)
self.__gpfY.pop(0)
self.__modelTimeIdx += 1
if len(self.__clusterFeatures) >= self.__winSize:
# Learn the models (align the data to input output format Y(k+1) = f(x(k),u(k+1)))
self.__inData.pop(0)
self.__outData.pop(0)
# self.__statData.pop()
# self.__clusterFeatures.pop()
# classify the data
clustersX = self.__myCluster.fit_predict(self.__clusterFeatures)
if self.verbose:
print("training state-space models using GPs")
for i in range(self.__Nc):
gprX = []
gprY = []
for j in range(len(clustersX) - 1):
if clustersX[j] == i:
gprX.append([self.__inData[j]] + self.__statData[j])
gprY.append(self.__outData[j])
gprX = np.array(gprX)
gprY = np.array(gprY)
gpmdl = gp.GPR()
m = gp.mean.Zero()
RBF_hyp_init = [0.5] * (
len(sysStat) + 2
) # [13.9310228936928,2.54640381722411,0.177686434357263,12.5490563084955,162.467937309584,3.38074333489536]
k = gp.cov.RBFard(D=None,
log_ell_list=RBF_hyp_init[:-1],
log_sigma=RBF_hyp_init[-1])
gpmdl.setPrior(mean=m, kernel=k)
if self.verbose:
print("training GP of mode: " + str(i))
# gpmdl.getPosterior(gprX,gprY)
gpmdl.setNoise(log_sigma=np.log(0.8))
gpmdl.setOptimizer('Minimize') # ('Minimize');
gpmdl.optimize(gprX, gprY) # ,numIterations=100)
self.__GPs.append(gpmdl)
if self.verbose:
print("training forecast Model using GP")
try:
k_f = gp.cov.RBF(log_ell=1, log_sigma=1)
self.__gpFmdl.setPrior(mean=gp.mean.Zero(), kernel=k_f)
self.__gpFmdl.setNoise(log_sigma=np.log(0.8))
self.__gpFmdl.setOptimizer('BFGS') # ('Minimize');
self.__gpFmdl.optimize(np.array(self.__gpfX),
np.array(
self.__gpfY)) # ,numIterations=100)
except:
print('can quasi-newton it (forecast)')
self.__gpFmdl = gp.GPR()
k_f = gp.cov.RBF(log_ell=1, log_sigma=1)
self.__gpFmdl.setPrior(mean=gp.mean.Zero(), kernel=k_f)
self.__gpFmdl.setNoise(log_sigma=np.log(0.8))
self.__gpFmdl.setPrior(mean=gp.mean.Zero(), kernel=k_f)
self.__gpFmdl.setOptimizer('Minimize') # ('Minimize');
self.__gpFmdl.optimize(
np.array(self.__gpfX),
np.array(self.__gpfY)) # , numIterations=100)
self.__sysState = self.__statData[-2]
self.__modelInitilization = 1
# plt.plot(Ttrain,Ytrain,'r')
Ttest = np.atleast_2d(range(nTtrain, nT)).T
# plt.plot(Ttest,Ytest,'b', Ttest,predict, 'g.')
# plt.show()
# Baseline error
#err = getError.getError(Ytest, predict, muTIM, S2TIM)
# print 'NLL, MSE, MAE:'
# print err[0], err[1], err[2]
# Train GPTS
covFunc = pyGPs.cov.RQ() + pyGPs.cov.Const() + pyGPs.cov.Noise()
model = pyGPs.GPR()
model.setPrior(kernel=covFunc)
#model.setScalePrior([1.0, 1.0])
# Learn the hyperparameters on the training data
model.setOptimizer("RTMinimize", 10)
model.optimize(Ttrain, Ytrain)
# Do the extrapolation
logthetaGPTS = model.covfunc.hyp
#(mu, sig2, df) = GPTSonline(Ytest, covFunc, logthetaGPTS,model.ScalePrior)
# Plot the stuff
plt.axis([0.0, 7.0, -4.0, 5.0])
plt.plot(Ttrain, Ytrain, 'r')
def __init__(self):
return
#def __init__(self, winSize = 600, gpWinSize = 50 ,featureWinSize=3 ,nu_cluster = 2,
# controlFeedback = 1, alpha = 3, beta = 1, sigma = 0.9, controllerDelay = 5 ):
# online Learning parameters and variables
self.__winSize = winSize
self.__gpWinSize = gpWinSize
self.__featureWinSize = featureWinSize
self.__modelInitilization = 0 # 0: not initialozed, 1: learn and predict
self.offlineInit = 0 # 1: use an offline data to init the model, 0: init online by buffering data
self.__fileName = "result04.csv"
# load data
self.__inData = []
self.__outData = []
self.__statData = []
self.__gpfX = []
self.__gpfY = []
self.__sysState = []
# Number of clusters
self.__Nc = nu_cluster
self.__clusterFeatures = []
self.__featurewin = []
self.__onlineCluster = 1
# model status
self.__modelTimeIdx = 0
self.__modelstate = 0
# model control output
self.__controlOut = []
self.__controlFeedback = controlFeedback
self.__alpha = alpha
self.__beta = beta
self.__sigma = sigma
self.__controllerDelay = controllerDelay
self.__controllerCounter = 0
# model objects
# state models
self.__GPs = []
# forecast model
self.__gpFmdl = gp.GPR()
# classifier model
self.__myCluster = KMeans(n_clusters=self.__Nc,
init='random',
random_state=0)
# variables to store prediction data for evaluation and plotting:
# forecasting workload variables
self.__In_mean = []
self.__In_si = []
self.__In_pred_data = []
# classification variables
self.__sysModes = []
# for output variables
self.__out_mean = []
self.__out_si = []
self.__out_pred_data = []
# for i in self.__Nc:
# self.__out_mean.append([])
# self.__out_si.append([])
# self.__out_pred_data.append([])
# other
self.verbose = 0
if self.offlineInit:
# pass the offline data to initialize the model
# input, output and model state indices in data
indxIn = 36 # range(0,9) # Input indices [1:9]
indxOut = 38 # range(9,18) # Output indices [10:18]
indxS = [18, 26, 27,
35] # range(18,36) # State indices [20:36]
inData = 0
outData = 0
statData = []
with open(self.__fileName, 'rt') as dataFile:
reader = csv.reader(dataFile, delimiter=',')
for row in reader:
inData = float(row[indxIn])
outData = float(row[indxOut])
statData = [float(row[i]) for i in indxS]
self.__initModel(inData, statData, outData)
if self.__modelInitilization:
break
def visualize_video(self, features, version, label, _fgset, _colordp,
param):
groundtruth = np.load(self.param_path + '/v' + str(version) + '_' +
self.GT)
_trainX = np.concatenate(features[0:features.shape[0]:2])
_trainY = np.concatenate(groundtruth[0:groundtruth.size:2])
testX = features[1:features.shape[0]:2]
testY = groundtruth[1:groundtruth.size:2]
np.savetxt(self.res_path + '/feature_' + label + '.txt',
np.hstack((_trainX, _trainY.reshape(-1, 1))),
fmt='%d')
print 'features.shape: ', features.shape, ', groundtruth.shape: ', groundtruth.shape
print '_trainX.shape: ', _trainX.shape, ', _trainY.shape: ', _trainY.shape
trainX, trainY = self.exclude_label(_trainX, _trainY, c=0)
PYGPR = 'gpr_' + label
KNR = 'knr_' + label
if files.isExist(self.res_path, PYGPR):
gprmodel = self.loadf(self.res_path, PYGPR)
knrmodel = self.loadf(self.res_path, KNR)
else:
print 'Learning GPR model'
gprmodel = pyGPs.GPR()
gprmodel.getPosterior(trainX, trainY)
gprmodel.optimize(trainX, trainY)
self.savef(self.res_path, PYGPR, gprmodel)
print 'Learning KNR model'
knrmodel = knr(trainX, trainY)
self.savef(self.res_path, KNR, knrmodel)
print 'Learning both GPR and KNR model is DONE.'
Y_pred = np.array([])
Y_sum_pred = []
Y_pred_frame = []
for x in testX:
ym, ys2, fm, fs2, lp = gprmodel.predict(np.array(x))
Y_pred = np.hstack((Y_pred, ym.reshape(ym.size)))
ym = ym.reshape(ym.size)
Y_sum_pred.append(sum(ym))
Y_pred_frame.append(ym)
Y_label = []
Y_sum_label = []
for y in testY:
Y_label += y
Y_sum_label.append(sum(y))
imgset = []
fgset = _fgset[1:len(_fgset) - 1]
colordp = _colordp[1:len(_colordp) - 1]
for i in range(len(fgset)):
rect, cont = self.segmentation_blob(fgset[i], param)
tmp = colordp[i].copy()
pred = Y_pred_frame[i]
gt = groundtruth[i]
for j in range(len(rect)):
r = rect[j]
cv2.rectangle(tmp, (r[0], r[2]), (r[1], r[3]), tools.green, 1)
msg_pred = 'Pred: ' + str(pred[j])
msg_gt = 'GT: ' + str(gt[j])
cv2.putText(tmp, msg_pred, (r[0], r[2]),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.0, tools.blue)
cv2.putText(tmp, msg_gt, (r[0] + 10, r[2]),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.0, tools.red)
imgset.append(tmp)
images.display_img(imgset, 300)
def ANM_predict_causality(self,
train_size=0.5,
independence_criterion='HSIC',
metric='linear'):
'''
Prediction of causality based on the bivariate additive noise model
Parameters
----------
independence_criterion :
kruskal for Kruskal-Wallis H-test,
HSIC for Hilbert-Schmidt Independence Criterion
Returns
-------
Causal-direction: 1 if X causes Y, or -1 if Y causes X
'''
Xtrain, Xtest, Ytrain, Ytest = train_test_split(self.X,
self.Y,
train_size=train_size)
#_gp = KernelRidge(kernel='rbf',degree=3)#GaussianProcess()#
#Forward case
#_gp.fit(Xtrain,Ytrain)
#errors_forward = _gp.predict(Xtest) - Ytest
_gp = pyGPs.GPR()
_gp.getPosterior(Xtrain, Ytrain)
_gp.optimize(Xtrain, Ytrain)
ym, ys2, fm, fs2, lp = _gp.predict(Xtest)
errors_forward = ym - Ytest
#Backward case
#_gp.fit(Ytrain,Xtrain)
#errors_backward = _gp.predict(Ytest) - Xtest
_gp = pyGPs.GPR()
_gp.getPosterior(Ytrain, Xtrain)
_gp.optimize(Ytrain, Xtrain)
ym, ys2, fm, fs2, lp = _gp.predict(Ytest)
errors_backward = ym - Xtest
#Independence score
forward_indep_pval = {
'kruskal': kruskal(errors_forward, Xtest)[1],
'HSIC': self.HilbertSchmidtNormIC(errors_forward, Xtest)[1]
}[independence_criterion]
backward_indep_pval = {
'kruskal': kruskal(errors_backward, Ytest)[1],
'HSIC': self.HilbertSchmidtNormIC(errors_backward, Ytest)[1]
}[independence_criterion]
#print 'Scores:', forward_indep_pval, backward_indep_pval
#Warning it should be <
if forward_indep_pval > backward_indep_pval:
self.causal_direction = 1
self.pvalscore = forward_indep_pval
else:
self.causal_direction = -1
self.pvalscore = backward_indep_pval
return {
'causal_direction': self.causal_direction,
'pvalscore': self.pvalscore,
'difways': abs(forward_indep_pval - backward_indep_pval)
}
def ANM_causation_score(self,
train_size=0.5,
independence_criterion='HSIC',
metric='linear',
regression_method='GP'):
'''
Measure how likely a given causal direction is true
Parameters
----------
train_size :
Fraction of given data used to training phase
independence_criterion :
kruskal for Kruskal-Wallis H-test,
HSIC for Hilbert-Schmidt Independence Criterion
metric :
linear, sigmoid, rbf, poly
kernel function to compute gramm matrix for HSIC
gaussian kernel is used in :
Nonlinear causal discovery with additive noise models
Patrik O. Hoyer et. al
Returns
-------
causal_strength: A float between 0. and 1.
'''
Xtrain, Xtest, Ytrain, Ytest = train_test_split(self.X,
self.Y,
train_size=train_size)
if regression_method == 'GP':
_gp = pyGPs.GPR() # specify model (GP regression)
_gp.getPosterior(
Xtrain,
Ytrain) # fit default model (mean zero & rbf kernel) with data
_gp.optimize(
Xtrain, Ytrain
) # optimize hyperparamters (default optimizer: single run minimize)
#Forward case
#_gp = KernelRidge(kernel='sigmoid',degree=3)
#_gp.fit(Xtrain,Ytrain)
ym, ys2, fm, fs2, lp = _gp.predict(Xtest)
#_gp.plot()
#errors_forward = _gp.predict(Xtest) - Ytest
errors_forward = ym - Ytest
else:
_gp = KernelRidge(kernel='sigmoid')
_gp.fit(Xtrain, Ytrain)
errors_forward = _gp.predict(Xtest) - Ytest
#Independence score
forward_indep_pval = {
'kruskal':
kruskal(errors_forward, Xtest)[1],
'HSIC':
self.HilbertSchmidtNormIC(errors_forward, Xtest, metric=metric)[1]
}[independence_criterion]
return {'causal_strength': forward_indep_pval}
temp = [trainingSet[i][5]]
y5.append(temp)
temp = [trainingSet[i][6]]
y6.append(temp)
x = np.array(X)
y0 = np.array(y0)
y1 = np.array(y1)
y2 = np.array(y2)
#m = pyGPs.mean.Zero()
#k = pyGPs.cov.RBFard(log_ell_list=[0.05,0.17], log_sigma=1.)
#model.setPrior(mean=m, kernel=k)
#model.setNoise( log_sigma = np.log(0.1) )
model1 = pyGPs.GPR() # model
model1.setData(x, y0)
model2 = pyGPs.GPR()
model2.setData(x, y1)
model3 = pyGPs.GPR()
model3.setData(x, y2)
host = ''
port = 8221
address = (host, port)
server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_socket.bind(address)
server_socket.listen(5)
def calculate_rmse_gp(vector_x,
vector_y,
weighted=True,
plot=False,
context=None,
optimization_params=None,
signed=False,
sample=None):
"""Calculate the root mean squared error.
:param vector_x: timestamps of the timeseries
:param vector_y: valueSet of the timeseries
:param weighted: weight RMSE wrt variance of prediction
:param plot: plot the expected function
:param context: (internal)
:param optimization_params:
:param signed: Add a sign to RMSE based on whether the prediction is on average higher or lower than the prediction
:param sample: Learn from sample of the data (int for min number, float for fraction, list for inidices)
:returns: list(idx,rmse), hyperparams, model
"""
if optimization_params is None:
optimization_params = {}
# setX = [preprocessing.scale(element )for element in vectorX]
# setY = preprocessing.scale(vector_y, axis=1)
vector_y_train = vector_y
vector_x_train = vector_x
if sample:
if type(sample) == float:
logger.debug("Sample series for training (ratio)")
vector_y_train = []
vector_x_train = []
for idx in random.sample(range(len(vector_y)),
k=int(len(vector_y) * sample)):
vector_y_train.append(vector_y[idx])
vector_x_train.append(vector_x[idx])
elif type(sample) == int:
logger.debug("Sample series for training (number)")
if len(vector_y) <= sample:
vector_y_train = vector_y
vector_x_train = vector_x
else:
vector_y_train = []
vector_x_train = []
for idx in random.sample(range(len(vector_y)), k=sample):
vector_y_train.append(vector_y[idx])
vector_x_train.append(vector_x[idx])
elif type(sample) == list:
logger.debug("Sample series for training (indices)")
vector_y_train = []
vector_x_train = []
for idx in sample:
vector_y_train.append(vector_y[idx])
vector_x_train.append(vector_x[idx])
model = pyGPs.GPR() # specify model (GP regression)
k = pyGPs.cov.Linear() + pyGPs.cov.RBF(
) # hyperparams will be set with optimizeHyperparameters method
model.setPrior(kernel=k)
hyperparams, model2 = gpe.optimizeHyperparameters(
optimization_params.get("initialHyperParameters",
[0.0000001, 0.0000001, 0.0000001]),
model,
vector_x_train,
vector_y_train,
bounds=optimization_params.get("bounds", [(None, 5), (None, 5),
(None, 5)]),
method=optimization_params.get("method", 'L-BFGS-B'))
logger.info('Hyperparameters used: {}'.format(hyperparams))
# mean (y_pred) variance (ys2), latent mean (fmu) variance (fs2), log predictive prob (lp)
y_pred, ys2, fm, fs2, lp = model2.predict(vector_x[0])
last_vector_x = vector_x[0]
rmse_data = []
for i in range(len(vector_y)):
if not np.all(np.equal(last_vector_x, vector_x[i])):
logger.debug("Recomputing prediction")
y_pred, ys2, fm, fs2, lp = model2.predict(vector_x[i])
last_vector_x = vector_x[i]
if weighted:
rmse = math.sqrt(
mean_squared_error(vector_y[i], y_pred,
(np.max(ys2) - ys2)) / np.max(ys2))
else:
rmse = math.sqrt(mean_squared_error(vector_y[i], y_pred))
if signed:
if np.mean(vector_y[i] - y_pred) < 0:
rmse = -rmse
rmse_data.append((i, rmse))
if plot:
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(14, 2))
xs = vector_x[0]
ym = y_pred
xss = np.reshape(xs, (xs.shape[0], ))
ymm = np.reshape(ym, (ym.shape[0], ))
ys22 = np.reshape(ys2, (ys2.shape[0], ))
for i in vector_y:
ax[0].plot(i, color='blue', alpha=0.2)
ax[0].set_title("Node {}".format(context["cum_depth"]))
ax[0].fill_between(xss,
ymm + 3. * np.sqrt(ys22),
ymm - 3. * np.sqrt(ys22),
facecolor=[0.7539, 0.89453125, 0.62890625, 1.0],
linewidth=0.5)
ax[0].plot(xss, ym, color='red', label="Prediction")
ax[0].legend()
rmse_list = [t[1] for t in rmse_data]
ax[1].hist(rmse_list, bins=100)
ax[1].vlines(np.mean(rmse_list), 0, 2, color="red")
ax[1].set_xlabel("RMSE")
ax[1].set_ylabel("#")
# plt.show(block=True)
return rmse_data, hyperparams, model2
def __updateModel(self, sysIn, sysStat, sysOut,sysClus):
# update the forecast model
if self.verbose:
print ("update forecast Model")
# add the new data and delete the oldest (slid the win)
gpfX = np.append(self.__gpFmdl.x, np.array([self.__modelTimeIdx]).reshape(-1, 1), axis=0)
gpfY = np.append(self.__gpFmdl.y, np.array([sysIn]).reshape(-1, 1), axis=0)
while gpfY.size > self.__gpin_winSize:
# delete oldest data
gpfX = np.delete(gpfX, 0, 0)
gpfY = np.delete(gpfY, 0, 0)
self.__modelTimeIdx += 1
# get the old hyp
hyp_f = self.__gpFmdl.covfunc.hyp
# relearn the model with the old hyp as a prior model
try:
self.__gpFmdl = gp.GPR()
k_f = gp.cov.RBF(log_ell=hyp_f[0], log_sigma=hyp_f[1])
self.__gpFmdl.setPrior(mean=gp.mean.Zero(), kernel=k_f)
self.__gpFmdl.setNoise(log_sigma=np.log(0.8))
self.__gpFmdl.setOptimizer('BFGS')
self.__gpFmdl.optimize(gpfX, gpfY)
except:
print('cannot BFGS it, forecast')
self.__gpFmdl = gp.GPR()
k_f = gp.cov.RBF(log_ell=hyp_f[0], log_sigma=hyp_f[1])
self.__gpFmdl.setPrior(mean=gp.mean.Zero(), kernel=k_f)
self.__gpFmdl.setNoise(log_sigma=np.log(0.8))
self.__gpFmdl.setOptimizer('Minimize')
self.__gpFmdl.optimize(gpfX, gpfY)
# Update cluster model
if self.__onlineCluster:
if self.verbose:
print ("update cluster Model ...")
self.__myCluster = KMeans(n_clusters=self.__Nc,
init=self.__myCluster.cluster_centers_,
random_state=0,n_init=1)
# update the cluster data (slid the window)
self.__clusData.append(sysClus)
self.__clusData.pop(0)
# update the clusterer
self.__myCluster.fit(self.__clusData)
# update system state GP model
if self.verbose:
print ("update system Models ...")
# Estiamte discrete Mode
predCluster = self.__myCluster.predict(np.array(sysClus).reshape(1, -1))
self.__modelstate = np.asscalar(predCluster[0])
self.__sysModes.append(self.__modelstate)
# pull the model used for the last prediction
gprMdl = self.__GPs[self.__modelstate]
newgprX = np.array([sysIn] + self.__sysState).reshape(1, -1)
gprX = np.append(gprMdl.x, newgprX, axis=0)
gprY = np.append(gprMdl.y, np.array([sysOut]).reshape(1, -1), axis=0)
# gprMdl.x = np.append(gprMdl.x, Xs, axis=0)
# gprMdl.y = np.append(gprMdl.y, [outData[i]], axis=0)
while gprY.size > self.__gpWinSize:
gprX = np.delete(gprX, 0, 0)
gprY = np.delete(gprY, 0, 0)
hyp = gprMdl.covfunc.hyp
gprMdl = gp.GPR()
m = gp.mean.Zero()
# k = gp.cov.SumOfKernel(gp.cov.RBFard(D=None, log_ell_list=hyp, log_sigma=1.),gp.cov.Noise(1))
k = gp.cov.RBFard(D=None, log_ell_list=hyp[:-1], log_sigma=hyp[-1])
gprMdl.setPrior(mean=m, kernel=k)
# gprMdl.getPosterior(gprX,gprY)
gprMdl.setNoise(log_sigma=np.log(0.81))
try:
gprMdl.setOptimizer('BFGS')
gprMdl.optimize(gprX, gprY)
except:
print('cannot BFGS it ')
gprMdl = gp.GPR()
m = gp.mean.Zero()
# k = gp.cov.SumOfKernel(gp.cov.RBFard(D=None, log_ell_list=hyp, log_sigma=1.),gp.cov.Noise(1))
k = gp.cov.RBFard(D=None, log_ell_list=hyp[:-1], log_sigma=hyp[-1])
gprMdl.setPrior(mean=m, kernel=k)
# gprMdl.getPosterior(gprX,gprY)
gprMdl.setNoise(log_sigma=np.log(0.81))
gprMdl.setOptimizer('Minimize')
gprMdl.optimize(gprX, gprY)
self.__GPs[self.__modelstate] = gprMdl
# Update system state
self.__sysState = sysStat
# save the data for Error calculation and prediction evaluation
self.__In_pred_data.append(sysIn)
self.__out_pred_data.append(sysOut)
def remove_confounds_fast(training_predictors, testing_predictors, training_data, testing_data, training_label, training_group_labels, normalisation, verbose) :
# start by checking all inputs
assert (np.shape(training_predictors)[0] == np.shape(training_data)[0]), 'Training predictors and training data must have same number of subjects'
assert (np.shape(testing_predictors)[0] == np.shape(testing_data)[0]), 'Testing predictors and testing data must have same number of subjects'
assert (np.shape(training_predictors)[1] == np.shape(testing_predictors)[1]), 'Training and testing predictors must have same number of variables'
assert (np.shape(training_data)[1] == np.shape(testing_data)[1]), 'Training and testing data must have same number of variables'
assert (len(training_group_labels) == np.shape(training_data)[0]), 'Training group labels must have length equal to number of training subjects'
# initialise corrected data
corrected_testing_data = np.zeros_like(testing_data)
corrected_training_data = np.zeros_like(training_data)
# normalise the training and testing predictors
if normalisation :
testing_predictors = testing_predictors - np.min(training_predictors, axis=0)
training_predictors = training_predictors - np.min(training_predictors, axis=0)
testing_predictors = testing_predictors.astype(float) / np.max(training_predictors, axis=0)
training_predictors = training_predictors.astype(float) / np.max(training_predictors, axis=0)
# do regression
n_variables = np.shape(training_data)[1]
# if training group label not equal to 0, just train on subjects with
# the given label
# first copy original training predictors and data for predictions
testing_training_predictors = training_predictors
testing_training_data = training_data
if not training_label == 0 :
training_predictors = training_predictors[training_group_labels == training_label, :]
training_data = training_data[training_group_labels == training_label, :]
# set up GP
# calculate distance matrix of training predictors to initialise RBF
dists = squareform(pdist(training_predictors))
# covariance is linear + RBF + noise
# these all have built-in scale so no need to introduce extra hyps
# set scale hyps to unity
# set RBF length hyp to log of median dist
k = pyGPs.cov.Linear(log_sigma=np.log(1.0)) + pyGPs.cov.RBF(log_ell=np.log(np.median(dists[:])), log_sigma=np.log(1.0)) + pyGPs.cov.Noise(log_sigma=np.log(1.0))
# zero mean
m = pyGPs.mean.Zero()
model = pyGPs.GPR()
model.setPrior(mean=m, kernel=k)
model.setNoise(log_sigma=np.log(np.std(training_data[:])))
# optimize the hyperparameters by maximizing log-likelihood over all
# variables
if verbose :
print('Optimizing hyperparameters...')
hyps_opt = minimize_Kostro.minimize_Kostro(model, training_predictors, training_data, 200)
if verbose :
print('Hyperparameters optimized!')
# set GP with optimized hyperparameters
# must convert arrays to list
model.covfunc.hyp = list(hyps_opt[:-1])
model.setNoise(log_sigma=np.log(np.std(training_data[:])))
# loop through variables, removing the effects of confounds on each one
for i in range(n_variables) :
if (i % 1000) == 0 and verbose :
print '%i features processed' % i
# targets are the i'th column of features
training_targets = training_data[:, i]
# set training data
model.setData(training_predictors, training_targets)
# make predictions on training data
ym, ys2, fm, fs2, lp = model.predict(testing_training_predictors)
# store residuals
corrected_training_data[:, i] = testing_training_data[:, i] - np.squeeze(ym)
# make predictions on testing data
ym, ys2, fm, fs2, lp = model.predict(testing_predictors)
# store residuals
corrected_testing_data[:, i] = testing_data[:, i] - np.squeeze(ym)
return corrected_training_data, corrected_testing_data
t_training = t[points_training]
w_training = workload[points_training]
#w_training = w_training/max(w_training)
tr_training = tr1[points_training]
# Data for validation
points_validation = np.arange(0,len(t),5)
t_validation = t[points_validation]
w_validation = workload[points_validation]
tr_validation = tr1[points_validation]
# Training of the GP model
# Learning
gp_system = gp.GPR()
gp_system.setOptimizer("Minimize", num_restarts=10)
gp_system.getPosterior(w_training, tr_training)
gp_system.optimize(w_training, tr_training)
#plt.figure()
gp_system.predict(np.sort(w_validation))
#gp_system.plot()
# Validation
tr_predicted = np.zeros(len(t_validation))
for i in np.arange(len(t_validation)):
gp_system.predict(np.array([w_validation[i]]))
tr_predicted[i] = np.asscalar(gp_system.ym)
if __name__ == '__main__':
data = sio.loadmat('airlinedata.mat')
x = np.atleast_2d(data['xtrain'])
y = np.atleast_2d(data['ytrain'])
xt = np.atleast_2d(data['xtest'])
yt = np.atleast_2d(data['ytest'])
# To get interpolation too
#xt = np.concatenate((x,xt))
#yt = np.concatenate((y,yt))
# Set some parameters
Q = 10
model = pyGPs.GPR() # start from a new model
# Specify non-default mean and covariance functions
# @SEE doc_kernel_mean for documentation of all kernels/means
m = pyGPs.mean.Zero()
for _ in range(10):
hyps = pyGPs.cov.initSMhypers(Q, x, y)
k = pyGPs.cov.SM(Q, hyps)
model.setPrior(kernel=k)
# Noise std. deviation
sn = 0.1
model.setNoise(log_sigma=np.log(sn))
# Instead of getPosterior(), which only fits data using given hyperparameters,
import matplotlib.pyplot as plt
import numpy as np
import pyGPs
demoData = np.load('../../data/regression_data.npz')
x = demoData['x']
y = demoData['y']
z = demoData['xstar']
model_full = pyGPs.GPR()
model_full.getPosterior(x, y)
model_full.optimize(x, y)
model_full.predict(z)
model_full.plot()
# Training Error
prediction_x = model_full.predict(x)[0]
error_x = np.linalg.norm(prediction_x - y, 2) / np.linalg.norm(y, 2)
print('Training Error: %e' % error_x)
# Spectrum
covariance = model_full.covfunc.getCovMatrix(x, x, mode='train')
u, s, v = np.linalg.svd(covariance)
x_axis = np.arange(1, covariance.shape[0] + 1)
plt.plot(x_axis, s, '-r')
plt.title('Spectrum for Full GP')
plt.xlabel('Dimension')
plt.ylabel('Singular Value')
plt.show()
