class GaussianProcessInterpolator:
def __init__(self, observations):
self.observations = observations
self.gaussian_process = GaussianProcess(corr='cubic',
theta0=1e-2,
thetaL=1e-4,
thetaU=1e-1,
random_start=100)
self._compute_model()
def _compute_model(self):
observation_points = []
observation_results = []
for entry in self.observations:
observation_points.append(entry[0])
observation_results.append(entry[1])
observation_points_array = np.atleast_2d(observation_points)
observation_results_array = np.array(observation_results).T
self.gaussian_process.fit(observation_points_array,
observation_results_array)
def compute_prediction(self, observation_points):
observation_points_array = np.atleast_2d(observation_points)
predicted_observation_results, MSE = self.gaussian_process.predict(
observation_points_array, eval_MSE=True)
return predicted_observation_results, MSE
def interpolate_measurements(measurements, interptype='griddata'):
pts, z = measurements_to_cartesian_points(measurements)
gridx, gridy = make_mesh_grid(pts)
if interptype == 'griddata':
grid = interpolate.griddata(pts,
z, (gridx, gridy),
method='linear',
fill_value=-3e30)
elif interptype == 'rbf':
ptx, pty = list(zip(*pts))
f = interpolate.Rbf(ptx, pty, z, function='linear')
grid = f(gridy, gridx)
elif interptype == 'gauss':
from sklearn.gaussian_process import GaussianProcess
ptx, pty = list(zip(*pts))
ptx = np.array(ptx)
pty = np.array(pty)
z = np.array(z)
print(math.sqrt(np.var(z)))
gp = GaussianProcess(regr='quadratic',
corr='cubic',
theta0=np.min(z),
thetaL=min(z),
thetaU=max(z),
nugget=0.05)
gp.fit(X=np.column_stack([pty, ptx]), y=z)
rr_cc_as_cols = np.column_stack([gridy.flatten(), gridx.flatten()])
grid = gp.predict(rr_cc_as_cols).reshape((ncol, nrow))
return gridx, gridy, grid
def nugget_kungfu(day=0, fx_id=0, hour=3, theta0=(0.4, 1.0)):
G = X.nm[day, fx_id, :, hour]
G_m = G.mean(axis=0)
G_s = G.std(axis=0)
from sklearn.gaussian_process.gaussian_process import MACHINE_EPSILON
nugget = (G_s / G_m) ** 2.0
mask = ~np.isfinite(nugget)
nugget[mask] = 10. * MACHINE_EPSILON
nugget = nugget.ravel()
est = GaussianProcess(corr='squared_exponential',
theta0=theta0,
#thetaL=(.5, 1.0), thetaU=(5.0, 10.0),
#random_start=100,
nugget=nugget,
)
est.fit(x, G_m.ravel())
print('est.theta_: %s' % str(est.theta_))
pred, sigma = est.predict(np.c_[new_lats.ravel(), new_lons.ravel()],
eval_MSE=True)
pred = pred.reshape((10 * lon.shape[0], 10 * lat.shape[0])).T
sigma = sigma.reshape((10 * lon.shape[0], 10 * lat.shape[0])).T
fig, ([ax1, ax2, ax3, ax4]) = plt.subplots(4, 1)
ax1.imshow(G_m, interpolation='none')
ax1.set_ylabel('Ens mean')
ax2.imshow(G_s, interpolation='none')
ax2.set_ylabel('Ens std')
ax3.imshow(pred, interpolation='none')
ax3.set_ylabel('GP mean')
ax4.imshow(sigma, interpolation='none')
ax4.set_ylabel('GP sigma')
def __init__(self, objective, noise, burn_in=2, grid=None):
"""
Initialize the model. Here dim is the dimensionality of the problem.
"""
# this is where the objective function stored.
self.objective = objective
self.burn_in = burn_in # check this many random points before fitting the GP
self.gp = GaussianProcess(theta0=.5, thetaL=.01, thetaU=10., nugget=noise)
# X and Y, this is where the training data for the GP stored.
self.X = np.zeros((0, objective.ndim))
self.Y = np.zeros((0, 1))
if grid:
self.grid = grid
else:
# If the user did not pass a grid, we construct a grid by using a
# sobol sequence.
self.grid = np.transpose(sobol.i4_sobol_generate(
objective.ndim, objective.ndim * 200, 7))
# expand the grid to cover the domain of the objective
d = self.objective.domain
self.grid = self.grid * (d[1] - d[0]) + d[0]
print self.grid
def test_2d(regr=regression.constant,
corr=correlation.squared_exponential,
random_start=10,
beta0=None):
# MLE estimation of a two-dimensional Gaussian Process model accounting for
# anisotropy. Check random start optimization.
# Test the interpolating property.
b, kappa, e = 5., .5, .1
g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e)**2.
X = np.array([[-4.61611719, -6.00099547], [4.10469096, 5.32782448],
[0.00000000, -0.50000000], [-6.17289014, -4.6984743],
[1.3109306, -6.93271427], [-5.03823144, 3.10584743],
[-2.87600388, 6.74310541], [5.21301203, 4.26386883]])
y = g(X).ravel()
thetaL = [1e-4] * 2
thetaU = [1e-1] * 2
gp = GaussianProcess(regr=regr,
corr=corr,
beta0=beta0,
theta0=[1e-2] * 2,
thetaL=thetaL,
thetaU=thetaU,
random_start=random_start,
verbose=False)
gp.fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.))
eps = np.finfo(gp.theta_.dtype).eps
assert_true(
np.all(gp.theta_ >= thetaL - eps)) # Lower bounds of hyperparameters
assert_true(
np.all(gp.theta_ <= thetaU + eps)) # Upper bounds of hyperparameters
def gaussian_process(nx, ny, x, y, x_min, y_min, dx, dy):
"""
Gausian process method. To replace kriging.
Description:
http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcess.html#sklearn.gaussian_process.GaussianProcess.predict
The scikit learn python library should be installed
Should be tested
"""
# Prediction is very sensitive to the parameters below, please use with care!
gp = GaussianProcess(regr='quadratic',
corr='cubic',
theta0=0.1,
thetaL=.001,
thetaU=1.,
nugget=0.01)
gp.fit(x, y)
x_grid_x = np.linspace(x_min, x_min + dx * nx, nx)
x_grid_y = np.linspace(y_min, y_min + dy * ny, ny)
xv, yv = np.meshgrid(x_grid_x, x_grid_y)
x_grid = np.dstack((xv.flatten(), yv.flatten()))[0]
grid = np.reshape(gp.predict(x_grid, eval_MSE=False, batch_size=None),
(ny, nx))
return grid
def ensemble(value, noise):
#mixture = GMM(C = 100)
mixture = GMM()
newdataset = addNoise(noise)
temp = np.copy(newdataset[:,-1])
newdataset[:,-1] = newdataset[:,2]
newdataset[:,2] = temp
#print newdataset[:,-1], newdataset[:,3]
for ensemble in range(0,value):
np.random.shuffle(newdataset)
train = np.copy(newdataset[0:-10,:])
test = np.copy(newdataset[-10:-1,:])
test_pred = np.copy(test)
mixture.fit(newdataset[0:-10,0:-2],newdataset[0:-10,-1])
preds = mixture.predict(newdataset[-10:-1,0:-2])
test_pred[:,-2:-1] = preds
errorabs = abs(dataset[-10:-1,-1]-preds)/(dataset[-10:-1,-1])
meanerrorabs = np.mean(errorabs)
stderrorabs = np.std(errorabs)
print preds
#print meanerrorabs, stderrorabs
#plt.plot(abs(dataset[-10:-1,-1]-preds))
#plt.ylim(-5e-12,5e-12)
#plt.scatter(dataset[-10:-1,0],dataset[-10:-1,-1])
#plt.plot(preds)
#plt.show()
np.savetxt('data/new_train_%d_snr_%d.csv'%(ensemble,noise),train,delimiter=',')
np.savetxt('data/new_test_%d_snr_%d.csv'%(ensemble,noise),test,delimiter=',')
np.savetxt('data/new_test_predict_%d_snr_%d.csv'%(ensemble,noise),test_pred,delimiter=',')
def kriging(self, X, y, X_pred):
"""Interpolate using Gaussian Process Regression (kriging).
Uses the GP pacakge from 'sklearn' to interpolate spatial data points
(2d).
Interpolation equal noiseless case, i.e., "almost" no uncertainty in
the observations.
Bounds are defined assuming anisotropy.
"""
# instanciate a Gaussian Process model
gp = GaussianProcess(regr=self.regr,
corr=self.corr,
theta0=self.theta0,
thetaL=self.thetaL,
thetaU=self.thetaU,
random_start=self.rand_start,
nugget=self.nugget,
verbose=True)
# fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(X, y)
# evaluate the prediction points (ask for MSE as well)
y_pred, MSE = gp.predict(X_pred, eval_MSE=True)
return [y_pred, np.sqrt(MSE)]
def crossValidation(self, n):
kf = KFold(len(self.trainX), n_folds = n)
total_error = 0
predictions = {}
if self.algorithm != 'gp':
for train,test in kf:
this_x = []
this_y = []
for i in train:
this_x.append(self.trainX[i])
this_y.append(self.trainY[i])
reg = self.model_for_algorithm()
reg.fit(this_x, this_y)
for test_i in test:
predicted = reg.predict(self.trainX[test_i])
predictions[test_i] = predicted
squared_error = (predicted - self.trainY[test_i])**2
total_error += squared_error
self.count_accuracy(predictions)
return total_error / len(self.trainX), predictions
else:
for train_idx, test_idx in kf:
X_train = self.trainX[train_idx]
y_train = self.trainY[train_idx]
gp = GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1)
gp.fit(X_train, y_train)
for test_i in test_idx:
predicted, sigma2 = gp.predict(self.trainX[test_i], eval_MSE=True)
predictions[test_i] = (predicted, sigma2, self.trainY[test_i])
sigma = np.sqrt(sigma2)
if self.trainY[test_i] > predicted + 1.96 * sigma or self.trainY[test_i] < predicted - 1.96 * sigma:
total_error += 1
return total_error / float(len(self.trainX)), predictions
def train_gaussian(path):
X, Y, weight = load_data(path, get_avg(path))
np.save("train_X", X)
np.save("train_Y", Y)
gp = GaussianProcess()
gp.fit(X, Y)
return gp
def test(filename, reg):
# initial the parameters
#(X, y) = data_read('./data/eta_reg_605603_p0.data');
# (X, y) = load_svmlight_file('./data/eta_reg_605603_p0.data');
(X, y) = load_svmlight_file(filename);
print("Data imported!");
# divide into training and test_pyGPs datasets
ratio = 0.8;
num = len(y);
idx = range(num);
random.shuffle(idx);
tn = int(np.floor(num*ratio));
tr_idx = idx[:tn]
te_idx = idx[tn:]
# X_train = X[tr_idx]
# y_train = y[tr_idx]
tnum = int(np.floor(0.2*num))
X_train = X[tr_idx[:tnum]]
y_train = y[tr_idx[:tnum]]
X_test = X[te_idx]
y_test = y[te_idx]
# train linear ridge regression model
print("Model training!");
p1 = float(reg);
clf = GaussianProcess(corr='squared_exponential');
# clf.fit(list(X_train), y_train);
clf.fit(X_train.toarray() , y_train);
abe = np.mean( abs(clf.predict(X_test.toarray()) - y_test)/y_test )
#np.mean((clf.predict(X_test) - y_test) ** 2))
print("Absolute error is: %.2f" % abe );
def check2d(X, regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
"""
MLE estimation of a two-dimensional Gaussian Process model accounting for
anisotropy. Check random start optimization.
Test the interpolating property.
"""
b, kappa, e = 5., .5, .1
g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2.
y = g(X).ravel()
thetaL = [1e-4] * 2
thetaU = [1e-1] * 2
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=[1e-2] * 2, thetaL=thetaL,
thetaU=thetaU,
random_start=random_start, verbose=False)
gp.fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.))
assert_true(np.all(gp.theta_ >= thetaL)) # Lower bounds of hyperparameters
assert_true(np.all(gp.theta_ <= thetaU)) # Upper bounds of hyperparameters
def test_2d_2d(regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
"""
MLE estimation of a two-dimensional Gaussian Process model accounting for
anisotropy. Check random start optimization.
Test the GP interpolation for 2D output
"""
b, kappa, e = 5., .5, .1
g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2.
f = lambda x: np.vstack((g(x), g(x))).T
X = np.array([[-4.61611719, -6.00099547],
[4.10469096, 5.32782448],
[0.00000000, -0.50000000],
[-6.17289014, -4.6984743],
[1.3109306, -6.93271427],
[-5.03823144, 3.10584743],
[-2.87600388, 6.74310541],
[5.21301203, 4.26386883]])
y = f(X)
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=[1e-2] * 2, thetaL=[1e-4] * 2,
thetaU=[1e-1] * 2,
random_start=random_start, verbose=False)
gp.fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.))
def optimize(self, f, beta=1, init_num=10, restarts=100, \
opt_fx=float('-inf'), torelance=1e-8, iteration=1000):
X = self.generate_random_variables(init_num)
Fx = [f(x) for x in X]
x_best = self.lower
fx_best = min(Fx)
acq_values = []
num_evals = 0
for i in range(iteration):
gp = GaussianProcess(theta0=[0.1]*self.dim)
gp.fit(X, Fx)
x_new, acq_value = self.max_acquisition(gp, beta=beta, restarts=restarts)
fx_new = f(x_new)
num_evals += 1
if fx_new < fx_best:
x_best = x_new
fx_best = fx_new
print("%d \t %s \t %s" % (i, fx_best, x_best))
if np.abs(fx_best - opt_fx) < torelance : break
#X = np.vstack((X, x_new))
#Fx = np.hstack((Fx, fx_new))
#acq_values.append(acq_value)
print("#Evals=%d \t\t f(x*)=%s" % (num_evals, str(fx_best)))
return (x_best, fx_best)
class GaussianProcessRigression(object): def __init__(self,data_frame,test_df): self.df=data_frame self.test=test_df def fitModel(self,predictors,output_var): self.predictors=predictors self.output_var=output_var self.gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1e-1, random_start=100) self.gp.fit(self.df[predictors].values,self.df[output_var].values) self.y_pred, self.MSE = self.gp.predict(self.test[predictors], eval_MSE=True) self.sigma = np.sqrt(self.MSE) #works only when you have single parameter for predictors only #as for multiple parameters doesn't make sense to make a 2D curve. def givePlot(self,xlabel='$x$',ylabel='$f(x)$'): fig = pl.figure() pl.plot(self.test[self.predictors], self.test[self.output_var], 'r:', label=u'Actual curve') # pl.plot(X, y, 'r.', markersize=10, label=u'Observations') pl.plot(self.test[self.predictors], self.y_pred, 'b-', label=u'Prediction') pl.fill(np.concatenate([self.test[self.predictors], self.test[self.output_var]]), \ np.concatenate([self.y_pred - 1.9600 * self.sigma, (self.y_pred + 1.9600 * self.sigma)[::-1]]), \ alpha=.5, fc='b', ec='None', label='95% confidence interval') pl.xlabel(xlabel) pl.ylabel(ylabel) pl.ylim(-10, 20) pl.legend(loc='upper left') pl.show()
def gp_lc_fit(t, m, e, npoints=10000, plot=True, theta=0.15):
'''
Use Gaussian process regression to fit a functional
form to a supernova lightcurve.
t, m, e: 1D arrays for time, mag, and error
theta: the autcorrelation timescale parameter. If the model needs to have more
flexture, increase theta. If it needs less, decrease theta.
returns: t, mag, err arrays of size npoints
'''
edge = 1.0 # predict beyond the data by this amount on either side
X = np.atleast_2d( t ).T # need a 2D array for skl procedures
y = np.array(m)
e = np.array(e)
x = np.atleast_2d( np.linspace(t[0]-edge, t[-1]+edge, npoints)).T
xf = x.flatten()
gp = GaussianProcess(regr='linear', nugget=(e/y)**2, theta0=theta)
gp.fit(X,y)
y_pred, MSE = gp.predict(x, eval_MSE=True)
ep = MSE**.5 # prediction error estimate
if plot:
plt.figure()
plt.errorbar( t, m, yerr=e, fmt='b.' )
plt.plot(xf, y_pred, 'g', lw=2)
plt.fill_between( xf, y_pred+ep, y_pred-ep, alpha=0.5, color='g' )
plt.gca().invert_yaxis()
plt.title('Photometry, GP model, and errors')
plt.show()
return xf, y_pred, ep
def _trainGP(self, colors, nfit=-1):
"""Train the mapping between eigenvalues and color via Gaussian Process
@param colors array of galaxy colors
@param nfit number of eigenvalues to use in the mapping (if =-1 use all)
"""
self._gp = GaussianProcess(corr=self._corr_type, theta0=self._theta0)
if (nfit < 0):
nfit = self.eigenvalue_coeffs.shape[1]
print "Using all", nfit, "eigenvalues in GP"
# BK note:
# Make sure we only include unique color values
# Used method for taking unique rows in array found here:
# http://stackoverflow.com/questions/16970982/find-unique-rows-in-numpy-array
data = colors
find_unique = np.ascontiguousarray(data).view(
np.dtype((np.void, data.dtype.itemsize * data.shape[1])))
unique_idx = np.unique(find_unique, return_index=True)[1]
print "Number of unique colors in SED set", len(
unique_idx), "total number of SEDs =", len(colors)
# Train and predict eigenvalues for this color set
self._gp.fit(colors[unique_idx],
self.eigenvalue_coeffs[unique_idx, :nfit])
class GaussianProcessInterpolator:
def __init__(self, observations):
self.observations = observations
self.gaussian_process = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1e-1, random_start=100)
self._compute_model()
def _compute_model(self):
observation_points = []
observation_results = []
for entry in self.observations:
observation_points.append(entry[0])
observation_results.append(entry[1])
observation_points_array = np.atleast_2d(observation_points)
observation_results_array = np.array(observation_results).T
self.gaussian_process.fit(observation_points_array, observation_results_array)
def compute_prediction(self, observation_points):
observation_points_array = np.atleast_2d(observation_points)
predicted_observation_results, MSE = self.gaussian_process.predict(observation_points_array, eval_MSE=True)
return predicted_observation_results, MSE
def test_2d_2d(regr=regression.constant,
corr=correlation.squared_exponential,
random_start=10,
beta0=None):
"""
MLE estimation of a two-dimensional Gaussian Process model accounting for
anisotropy. Check random start optimization.
Test the GP interpolation for 2D output
"""
b, kappa, e = 5., .5, .1
g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e)**2.
f = lambda x: np.vstack((g(x), g(x))).T
X = np.array([[-4.61611719, -6.00099547], [4.10469096, 5.32782448],
[0.00000000, -0.50000000], [-6.17289014, -4.6984743],
[1.3109306, -6.93271427], [-5.03823144, 3.10584743],
[-2.87600388, 6.74310541], [5.21301203, 4.26386883]])
y = f(X)
gp = GaussianProcess(regr=regr,
corr=corr,
beta0=beta0,
theta0=[1e-2] * 2,
thetaL=[1e-4] * 2,
thetaU=[1e-1] * 2,
random_start=random_start,
verbose=False)
gp.fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.))
def gaussian_process(x_train, y_train, x_test):
def vector_2d(array):
return np.array(array).reshape((-1, 1))
import warnings
def fxn():
warnings.warn("deprecated", DeprecationWarning)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
x_train = vector_2d(x_train)
y_train = vector_2d(y_train)
x_test = vector_2d(x_test)
# Train gaussian process
gp = GaussianProcess(corr='squared_exponential',
theta0=1e-1, thetaL=1e-3, thetaU=1)
gp.fit(x_train, y_train)
# Get mean and standard deviation for each possible
# number of hidden units
y_mean, y_var = gp.predict(x_test, eval_MSE=True)
y_std = np.sqrt(vector_2d(y_var))
return y_mean, y_std
def noiseless():
X = np.atleast_2d([1.,3.,5.,6.,7.,8.,]).T
# observations
y = f(X).ravel() # reshape to 1-D
# mesh the input space to cover all points to predict f(x) and the MSE
x = np.atleast_2d(np.linspace(0,10,1000)).T # and flatten
# instantiate gauss process
gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4,
thetaU=1e-1, random_start=100)
# fit to data using maximum likelihood estimation of params
gp.fit(X,y)
# make predictions on meshed x-axis, also return MSE
y_pred, MSE = gp.predict(x, eval_MSE=True)
sigma = np.sqrt(MSE)
# plot
fig = plt.figure()
plt.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
plt.plot(X, y, 'r.', markersize=10, label=u'Observations')
plt.plot(x, y_pred, 'b-', label=u'Prediction')
# fill the space between the +/-MSE
plt.fill(np.concatenate([x, x[::-1]]), # reverse order of x
np.concatenate([y_pred - 1.9600 * sigma,
(y_pred + 1.9600 * sigma)[::-1]]),
alpha=0.5, fc='b', ec='None', label='95% confidence interval')
# shade, fill color, edge color
plt.title('Noiseless case')
plt.xlabel('$x$')
plt.ylabel('$f(x)$')
plt.ylim(-10, 20)
plt.legend(loc='upper left')
return
def train_gaussian(path):
X, Y, weight = load_data(path, get_avg(path))
np.save("train_X", X)
np.save("train_Y", Y)
gp = GaussianProcess()
gp.fit(X, Y)
return gp
def update_GP(self,meas_locations):
# define grid to evaluate stuff on
#res = 100
sensornoise=.000001
noise= sensornoise*np.random.randn(meas_locations.shape[0])
measurements = self.simulatedsurface(meas_locations) + noise
print meas_locations
print measurements
[self.meas_locations,self.new_measurements]=self.averageduplicates(meas_locations,measurements)
# Instanciate and fit Gaussian Process Model
gp = GaussianProcess(corr='squared_exponential',
#theta0=10e-1,
#thetaL=10e-1, ##thetaU=1e-1,
nugget=(sensornoise/self.new_measurements) ** 2
)
# Observations
print noise
# Don't perform MLE or you'll get a perfect prediction for this simple example!
gp.fit(self.meas_locations, self.new_measurements)
#evaluate the prediction and its MSE on a grid
y_pred, MSE = gp.predict(self.xgrid, eval_MSE=True)
sigma = np.sqrt(MSE)
y_pred = y_pred.reshape((self.res, self.res))
sigma = sigma.reshape((self.res, self.res))
self.model=y_pred
self.uncertainty=sigma
return [y_pred,sigma]
def gaussian_fit_likelihood(x, y):
# Remove duplicates
n_dupl = 0
d = dict()
for i in range(len(x)):
try:
if d[x[i]] != y[i]:
n_dupl += 1
d.pop(x[i], None)
except:
d[x[i]] = y[i]
ret = [n_dupl]
try:
newX = np.atleast_2d(d.keys()).T
newY = np.array(d.values()).ravel()
g = GaussianProcess(theta0=1e5, thetaL=1e-4, thetaU=1e-1)
#g = GaussianProcess()
g.fit(newX, newY)
err = newY - g.predict(newX)
p = pearsonr(err, newX)
ret += [g.reduced_likelihood_function_value_]
ret += p
except:
#fp = open("bad_pt.txt", "a")
#fp.write("1")
#fp.close()
ret += [0.0, 0.0, 0.0]
print ret
return ret
def kriging(self, X, y, X_pred):
"""Interpolate using Gaussian Process Regression (kriging).
Uses the GP pacakge from 'sklearn' to interpolate spatial data points
(2d).
Interpolation equal noiseless case, i.e., "almost" no uncertainty in
the observations.
Bounds are defined assuming anisotropy.
"""
# instanciate a Gaussian Process model
gp = GaussianProcess(regr=self.regr, corr=self.corr,
theta0=self.theta0, thetaL=self.thetaL,
thetaU=self.thetaU, random_start=self.rand_start,
nugget=self.nugget, verbose=True)
# fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(X, y)
# evaluate the prediction points (ask for MSE as well)
y_pred, MSE = gp.predict(X_pred, eval_MSE=True)
return [y_pred, np.sqrt(MSE)]
def test_2d(regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
# MLE estimation of a two-dimensional Gaussian Process model accounting for
# anisotropy. Check random start optimization.
# Test the interpolating property.
b, kappa, e = 5., .5, .1
g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2.
X = np.array([[-4.61611719, -6.00099547],
[4.10469096, 5.32782448],
[0.00000000, -0.50000000],
[-6.17289014, -4.6984743],
[1.3109306, -6.93271427],
[-5.03823144, 3.10584743],
[-2.87600388, 6.74310541],
[5.21301203, 4.26386883]])
y = g(X).ravel()
thetaL = [1e-4] * 2
thetaU = [1e-1] * 2
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=[1e-2] * 2, thetaL=thetaL,
thetaU=thetaU,
random_start=random_start, verbose=False)
gp.fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.))
eps = np.finfo(gp.theta_.dtype).eps
assert_true(np.all(gp.theta_ >= thetaL - eps)) # Lower bounds of hyperparameters
assert_true(np.all(gp.theta_ <= thetaU + eps)) # Upper bounds of hyperparameters
def initGP():
"""Do simulations with random pi,z and create GP, X, y"""
poolsize = 68
pool = Pool.spawn(Genome.open(NN_STRUCTURE_FILE), poolsize, std=10)
X = []
for i, org in enumerate(pool):
org.mutate()
genome = org.genome
w = genome.weights
z = [np.random.uniform(0, 0.3)]
reward = cliff(genome, z)
while reward <= 0 and len(X) < poolsize / 2:
#Train input policies to reach the goal.
org.mutate()
genome = org.genome
w = genome.weights
reward = cliff(genome, z)
if not len(X):
X = np.atleast_2d(w + z)
y = np.atleast_2d([reward])
else:
X = np.append(X, [w + z], axis=0)
y = np.append(y, [reward])
# Initialize GP with kernel parameters.
GP = GaussianProcess(theta0=0.1, thetaL=.001, thetaU=1.)
GP.fit(X, y)
return GP, X, y
def GaussianProcess_interp(points, values, xi, theta0=0.1, thetaL=0.001, thetaU=1.0, nugget=0.01):
x_points = np.array(points)[:, 0]
y_points = np.array(points)[:, 1]
gp = GaussianProcess(theta0=theta0, thetaL=thetaL, thetaU=thetaU, nugget=nugget)
gp.fit(X=np.column_stack([x_points, y_points]), y=np.array(values))
xi_as_cols = np.column_stack([xi[0].flatten(), xi[1].flatten()])
z = gp.predict(xi_as_cols).reshape(xi[0].shape)
return z
def __init__(self, observations):
self.observations = observations
self.gaussian_process = GaussianProcess(corr='cubic',
theta0=1e-2,
thetaL=1e-4,
thetaU=1e-1,
random_start=100)
self._compute_model()
def __init__(self,data,theta0=None,thetaL_r=None,thetaU_r=None,**ka):
self.data = data
gp = GaussianProcess(theta0=theta0,thetaL=thetaL_r*theta0,thetaU=thetaU_r*theta0,**ka)
gp.fit(data.x[:,newaxis],data.y)
def q(x,regr=gp.get_params()['regr'],beta=gp.reduced_likelihood_function()[1]['beta']):
return dot(regr(x),beta)
self.q = q
self.predict = gp.predict
def _predict_coordinate(segment, coord, times, t1, sigma=10., **kwargs):
times = np.atleast_2d(times).T
prev = segment[coord]
nugget = (sigma / (prev + sigma)) ** 2
gp = GaussianProcess(nugget=nugget, **kwargs)
gp.fit(times, prev)
return gp.predict(t1, eval_MSE=True)
def learning(regression,correlation,x_train,y_train):
print("Learning")
X_train, Y_train = numpy.asarray(x_train) , numpy.asarray(y_train)
gp = GaussianProcess(corr=correlation, normalize=True, regr=regression, thetaL=1e-2, thetaU=1.0)
gp.fit(X_train, Y_train)
return gp
def gaussReg(metrics, densities):
# metrics = [[0, 0], [2, 2]] #List of lists of metrics calculated
# densities = [0.5, 2.5] #The corresponding densities
gp = GaussianProcess(corr='absolute_exponential', theta0=1e-1,
thetaL=1e-3, thetaU=1,
random_start=100) #Change these parameters to get better fit...
gp.fit(metrics, densities)
return gp
def gaussReg(metrics, densities):
# metrics = [[0, 0], [2, 2]] #List of lists of metrics calculated
# densities = [0.5, 2.5] #The corresponding densities
gp = GaussianProcess(corr='absolute_exponential', theta0=1e-1,
thetaL=1e-3, thetaU=1,
random_start=100) #Change these parameters to get better fit...
gp.fit(metrics, densities)
return gp
def gpTest1():
xx = np.array([[10.]])
X = np.array([[1., 3., 5., 6., 7., 8., 9.]]).T
y = (X * np.sin(X)).ravel()
gp = GaussianProcess(theta0=0.1, thetaL=.001, thetaU=1.)
gp.fit(X, y)
p = gp.predict(xx)
print(X, y)
print(p, xx * np.sin(xx))
def Gaussian_Process_Regression(features, target):
print("===== GaussianProcess =====")
print("[INFO] Training the Classifier")
classifier = GaussianProcess(theta0=0.1, thetaL=0.001, thetaU=1.0)
classifier.fit(features, target)
print("Saving the classifier")
data_io.save_model(classifier)
def Gaussian_Process_Regression(features, target):
print("===== GaussianProcess =====")
print("[INFO] Training the Classifier")
classifier = GaussianProcess(theta0=0.1, thetaL=0.001, thetaU=1.0)
classifier.fit(features, target)
print("Saving the classifier")
data_io.save_model(classifier)
def get_best_params(model,
training,
testing,
non_relevant_count=100,
**kwargs):
"""
Search for the best set of parameters in the model
use ParameterTuning().getBestParameters(model,parA=(0,10,0.1,3)...)
(min,max,step,default)
"""
# Create a grid of parameters
kwargs = kwargs or model.param_details()
grid = zip(
*(x.flat
for x in np.mgrid[[slice(*row[:3]) for row in kwargs.values()]]))
m_instance = model()
values = {
k: ParameterTuning.tune(m_instance, training, testing,
non_relevant_count,
**dict(zip(kwargs.keys()[:2], k)))
for k in zip(*(v[:2] for v in kwargs.values()))
}
gp = GaussianProcess(theta0=.1, thetaL=.001, thetaU=5.)
# To make it reasonable we limit the number of iterations
for i in xrange(0, ParameterTuning.__max_iterations):
# Get a list of parameters and the correspondent result
param, response = zip(*values.items())
# Fit the GaussianProcess model with the parameters and results
gp.fit(np.array(param), np.array(response).T)
# Get prediction
y_predicted, mse = gp.predict(grid, eval_MSE=True)
# get upper confidence interval. 2.576 z-score corresponds to 99th
# percentile
ucb_u = y_predicted + np.sqrt(mse) * ParameterTuning.__z_score
next_list = zip(ucb_u, grid)
next_list.sort(reverse=True)
new_x = next_list[0][1]
if new_x not in values:
values[new_x] = ParameterTuning.tune(
m_instance, training, testing,
**{k: v
for k, v in zip(kwargs, new_x)})
else:
break
sv = sorted(values.items(), cmp=lambda x, y: cmp(y[1], x[1]))
assert sv[0][1] > sv[-1][1], "Sorted from lowest to highest"
return {k: v for k, v in zip(kwargs, sv[0][0])}
def run_test(training_size,prediction_size,function_name,corr_kernel,n_cluster,prior='GCP'): scoring_function = functions[function_name] parameter_bounds = all_parameter_bounds[function_name] x_training = [] y_training = [] for i in range(training_size): x = [np.random.uniform(parameter_bounds[j][0],parameter_bounds[j][1]) for j in range(parameter_bounds.shape[0])] x_training.append(x) y_training.append(scoring_function(x)[0]) if(isInt[function_name]): x_training,y_training = compute_unique2( np.asarray(x_training,dtype=np.int32) , np.asarray( y_training) ) candidates = [] real_y = [] for i in range(prediction_size): x = [np.random.uniform(parameter_bounds[j][0],parameter_bounds[j][1]) for j in range(parameter_bounds.shape[0])] candidates.append(x) real_y.append(scoring_function(x)[0]) real_y = np.asarray(real_y) if(isInt[function_name]): candidates = np.asarray(candidates,dtype=np.int32) if(prior == 'GP'): gp = GaussianProcess(theta0=.1 *np.ones(parameter_bounds.shape[0]), thetaL=0.001 * np.ones(parameter_bounds.shape[0]), thetaU=10. * np.ones(parameter_bounds.shape[0]), random_start=5, nugget=nugget) gp.fit(x_training,y_training) pred = gp.predict(candidates) likelihood = gp.reduced_likelihood_function_value_ else: gcp = GaussianCopulaProcess(nugget=nugget, corr=corr_kernel, random_start=5, normalize=True, coef_latent_mapping=coef_latent_mapping, n_clusters=n_clusters) gcp.fit(x_training,y_training) likelihood = gcp.reduced_likelihood_function_value_ if not (integratedPrediction): pred = gcp.predict(candidates) else: pred,_,_,_ = gcp.predict(candidates, eval_MSE = True, eval_confidence_bounds=True, integratedPrediction=True) mse = np.mean( (pred - real_y)**2. ) # Normalize mse = mse / ( np.std(real_y) **2. ) likelihood = np.exp(likelihood) return [mse,likelihood]
def gaussReg(metrics, densities):
"""Runs a Gaussian Regression algorithm on metrics, and array of metrics,
and densities, the corresponding densities."""
# metrics = [[0, 0], [2, 2]] #List of lists of metrics calculated
# densities = [0.5, 2.5] #The corresponding densities
gp = GaussianProcess( regr = 'linear', corr = 'absolute_exponential', theta0 = 1, thetaL=1, thetaU=10) #Change these parameters to get better fit...
gp.fit(metrics, densities)
return gp
def getAllData():
global allData, costModel, costModelInputScaler, costModelOutputScaler
if not allData or not costModel:
## COST MODEL
spamReader = csv.reader(open('time_results.csv', 'rb'), delimiter=';', quotechar='"')
x = []
y = []
for row in spamReader:
x.append([float(row[1]),float(row[2])])
y.append([float(row[3]) + random.random()])
x = array(x)
y = array(y)
input_scaler = preprocessing.StandardScaler().fit(x)
scaled_training_set = input_scaler.transform(x)
# Scale training data
output_scaler = preprocessing.StandardScaler(with_std=False).fit(y)
adjusted_training_fitness = output_scaler.transform(y)
regr = GaussianProcess(corr='squared_exponential', theta0=1e-1,
thetaL=1e-5, thetaU=3,
random_start=400)
regr.fit(scaled_training_set, adjusted_training_fitness)
costModel = regr
costModelInputScaler = input_scaler
costModelOutputScaler = output_scaler
## cores, accuracy, exeuction time
spamReader = csv.reader(open('AnsonCores.csv', 'rb'), delimiter=',', quotechar='"')
cores = {11:{}}
for row in spamReader:
cores[11][int(row[1])] = int(row[0])
maxcores = cores
spamReader = csv.reader(open('AnsonExec.csv', 'rb'), delimiter=';', quotechar='"')
allData = {}
for row in spamReader:
row_0 = int(row[0])
row_1 = int(row[1])
row_2 = int(row[2])
row_3 = float(row[3])
row_4 = float(row[4])
data = [cores[row_0][row_1],row_3,row_4]
try:
try:
allData[row_0][row_1][row_2] = data
except:
allData[row_0][row_1] = {row_2:data}
except:
allData[row_0] = {row_1:{row_2:data}}
#spamReader.close()
#print allData
return allData
def make_a_perfect_model(pairNo, x, X, y):
"""Make a GaussianProcess model for data without noise (It
complains for TDC dataset though!)"""
gp = GaussianProcess(theta0=1e-3, thetaL=1e-3, thetaU=1, random_start=500)
# Fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(X, y)
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, MSE = gp.predict(x, eval_MSE=True)
sigma = np.sqrt(MSE)
return gp, y_pred, sigma
def compareStats(rawData, theta):
data = [[rawData["lon"][i], rawData["lat"][i]] for i in range(len(rawData["lon"]))]
labels = rawData["classif"]
data, labels = mapping.cleanDoubles(data, labels)
rfc = GaussianProcess(regr="linear", theta0=theta)
rfc.fit(data, labels)
scores = cross_val_score(rfc, data, labels, cv=5)
print ("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
def learning(regression,correlation,trainingsize,Quantum_enrgy,Parameters):
x_train, y_train, x_test, y_test = load_data(Quantum_enrgy,Parameters,trainingsize)
print len(x_train)
print len(y_train)
X_train, y_train = numpy.asarray(x_train) , numpy.asarray(y_train)
X_val, y_val = numpy.asarray(x_test) , numpy.asarray(y_test)
print("Generating GP")
gp = GaussianProcess(corr=correlation, normalize=True, regr=regression, thetaL=1e-2, thetaU=1.0)
gp.fit(X_train, y_train)
return gp, x_train, y_train, x_test, y_test
class GaussianProcess(object):
def __init__(self, nugget=0.1):
self.nugget = nugget
def fit(self, chips):
X = pandas.DataFrame([[chip.X, chip.Y] for chip in chips])
y = [chip.gnd for chip in chips]
self.gp = GaussianProcess(nugget=self.nugget)
self.gp.fit(X, y)
def predict(self, chip):
return self.gp.predict([chip.X, chip.Y])
def gpo1d(objective): besto = 0.0 bestp = None D = 1 X = [] y = [] params = abs(rand(D)) * 10.0 X.append(params) y.append(objective([params, params])) params = abs(rand(D)) * 10.0 X.append(params) y.append(objective([params, params])) print "X = ", X print "y = ", y while(True): gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1e-1, random_start=100) gp.fit(X, y) #XX, YY = np.meshgrid(np.linspace(0, 10, 20), np.linspace(0, 10, 20)) #print XX XX = numpy.linspace(0, 10, 100) y_pred, mse = gp.predict(np.c_[XX], eval_MSE=True) sigma = np.sqrt(mse) #Z = np.array(Z) #Z = Z.reshape(XX.shape) pl.plot(X,y, 'xk') pl.plot(XX, y_pred, 'b-', label=u'Prediction') pl.fill(np.concatenate([XX, XX[::-1]]), np.concatenate([y_pred - 1.9600 * sigma, (y_pred + 1.9600 * sigma)[::-1]]), alpha=.5, fc='b', ec='None', label='95% confidence interval') #CS = pl.contour(XX, YY, Z, 20, colours='k') pl.show() # Find next point to evaluate # Evaluate and append to X and y for k in xrange(2): params = abs(rand(D)) * 10.0 X.append(params) y.append(objective([params, params])) return bestp
class GaussianProcess(object):
def __init__(self, nugget=0.1):
self.nugget = nugget
def fit(self, chips):
X = pandas.DataFrame([[chip.X, chip.Y] for chip in chips])
y = [chip.gnd for chip in chips]
self.gp = GaussianProcess(nugget=self.nugget)
self.gp.fit(X, y)
def predict(self, chip):
return self.gp.predict([chip.X, chip.Y])
def fit_GP(all_sp, miss_rate, train_rate):
# seperate train and test_pyGPs data, unobserved speeds are set as -1
train_sp, test_sp = sep_train(all_sp, miss_rate, train_rate)
# calculate historical mean
his_mean = calc_mean(train_sp)
# fit and predict
mape_dates = defaultdict(lambda: 0)
rmse_dates = defaultdict(lambda: 0)
for key in test_sp:
# X: historical mean, Y: current speed
miss_links = list()
miss_true_sp = list()
miss_mean_sp = list()
obser_links = list()
obser_true_sp = list() # Y
obser_mean_sp = list() # X
for l in range(len(test_sp[key])):
if test_sp[key][l] == -1:
miss_links.append(l)
miss_true_sp.append(all_sp[int(key[0])][int(key[1])][l])
miss_mean_sp.append(his_mean[l])
else:
obser_links.append(l)
obser_true_sp.append(all_sp[int(key[0])][int(key[1])][l])
obser_mean_sp.append(his_mean[l])
X = np.atleast_2d(obser_mean_sp).T
Y = np.array(obser_true_sp).T
gp = GaussianProcess(corr="absolute_exponential")
gp.fit(X, Y)
x = np.atleast_2d(miss_mean_sp).T
predict_sp, MSE = gp.predict(x, eval_MSE=True)
sigma = np.sqrt(MSE)
mape = 0.0
rmse = 0.0
for i in range(len(miss_links)):
rmse += (predict_sp[i] - miss_true_sp[i]) ** 2
mape += np.abs(predict_sp[i] - miss_true_sp[i]) / miss_true_sp[i]
rmse = np.sqrt(rmse / len(miss_links))
mape = mape / len(miss_links)
mape_dates[key] = mape
rmse_dates[key] = rmse
return (mape_dates, rmse_dates)
def test_1d(regr=regression.constant, corr=correlation.squared_exponential,
random_start=10, beta0=None):
# MLE estimation of a one-dimensional Gaussian Process model.
# Check random start optimization.
# Test the interpolating property.
gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0,
theta0=1e-2, thetaL=1e-4, thetaU=1e-1,
random_start=random_start, verbose=False).fit(X, y)
y_pred, MSE = gp.predict(X, eval_MSE=True)
y2_pred, MSE2 = gp.predict(X2, eval_MSE=True)
assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.)
and np.allclose(MSE2, 0., atol=10))
def create_gp_and_fit(x, y, max_try=100):
# この辺怪しい
theta0 = 0.1
for i in range(max_try + 1):
try:
gp = GaussianProcess(theta0=theta0)
gp.fit(x, y)
return gp
except Exception as e:
theta0 *= 10
if i == max_try:
print(theta0)
raise e
def noisy():
X = np.linspace(0.1, 9.9, 20)
X = np.atleast_2d(X).T
# observations and noise
y = f(X).ravel()
dy = 0.5 + 1.0 * np.random.random(y.shape)
noise = np.random.normal(0, dy)
y += noise
# mesh the input space for evaluation of the function & its prediction and MSE
x = np.atleast_2d(np.linspace(0, 10, 1000)).T
# instantiate a gauss process
gp = GaussianProcess(corr='squared_exponential',
theta0=1e-1,
thetaL=1e-3,
thetaU=1,
nugget=(dy / y)**2,
random_start=100)
# nugget specifies std of noise, Tikhonov regularization
# allows robust recovery of underlying function
# from noisy data
# fit to GP using maximum likelihood estimation of params
gp.fit(X, y)
# make predictions on meshed x-axis
y_pred, MSE = gp.predict(x, eval_MSE=True)
sigma = np.sqrt(MSE)
# plot
fig = plt.figure()
plt.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
plt.errorbar(X.ravel(),
y,
dy,
fmt='r.',
markersize=10,
label=u'Observations')
plt.plot(x, y_pred, 'b-', label=u'Prediction')
plt.fill(
np.concatenate([x, x[::-1]]), # reverse order of x
np.concatenate(
[y_pred - 1.9600 * sigma, (y_pred + 1.9600 * sigma)[::-1]]),
alpha=0.5,
fc='b',
ec='None',
label='95% confidence interval')
# shade, fill color, edge color
plt.title('Noisy case')
plt.xlabel('$x$')
plt.ylabel('$f(x)$')
plt.ylim(-10, 20)
plt.legend(loc='upper left')
return
def next(grid, candidates, pending, complete, completed_values):
gp = GaussianProcess(random_start=10, nugget=1e-6)
gp.fit(_encode_categorical_df(complete, grid), completed_values)
if pending.shape[0]:
# Generate fantasies for pending
mean, variance = gp.predict(_encode_categorical_df(pending, grid),
eval_MSE=True)
pending_value_estimation = pd.Series(mean + np.sqrt(variance) *
npr.randn(mean.shape[0]))
gp.fit(_encode_categorical_df(complete.append(pending), grid),
completed_values.append(pending_value_estimation))
# Predict the marginal means and variances at candidates.
mean, variance = gp.predict(_encode_categorical_df(candidates, grid),
eval_MSE=True)
best = np.min(completed_values)
func_s = np.sqrt(variance) + 0.0001
Z = (best - mean) / func_s
ncdf = sps.norm.cdf(Z)
npdf = sps.norm.pdf(Z)
ei = func_s * (Z * ncdf + npdf)
best_cand = np.argmax(ei)
return (best_cand, grid)
def test_mse_solving():
# test the MSE estimate to be sane.
# non-regression test for ignoring off-diagonals of feature covariance,
# testing with nugget that renders covariance useless, only
# using the mean function, with low effective rank of data
gp = GaussianProcess(corr='absolute_exponential', theta0=1e-4,
thetaL=1e-12, thetaU=1e-2, nugget=1e-2,
optimizer='Welch', regr="linear", random_state=0)
X, y = make_regression(n_informative=3, n_features=60, noise=50,
random_state=0, effective_rank=1)
gp.fit(X, y)
assert_greater(1000, gp.predict(X, eval_MSE=True)[1].mean())
class COGP(object):
def __init__(self, func, initial, minimize=True, storeAllEvaluations=True,
storeAllEvaluated=True, maxEvaluations=10):
self.func = func
self.X = np.array(initial)
self.y = np.array([func(el) for el in initial])
LEFT = np.min(initial)
RIGHT = np.max(initial)
self.x = [i for i in itertools.product(np.arange(LEFT, RIGHT, 0.1),
repeat=len(initial[0]))]
self.fmin = np.min(self.y)
self.argmin = self.X[np.argmin(self.y)]
self.gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4,
thetaU=1e-1)
self.storeAllEvaluations = storeAllEvaluations
self.storeAllEvaluated = storeAllEvaluated
if storeAllEvaluations:
self._allEvaluations = []
if storeAllEvaluated:
self._allEvaluated = []
self.max_evaluations = maxEvaluations
self.time = 0
def learn(self):
wall_time = time.time()
for step in xrange(self.max_evaluations):
try:
self.gp.fit(self.X, self.y)
except:
break
y_pred, MSE = self.gp.predict(self.x, eval_MSE=True)
s = (self.fmin-y_pred) / np.sqrt(MSE)
argm = np.argmax(MSE * (s * norm.cdf(s) + norm.pdf(s)))
self.X = np.vstack([self.X, self.x[argm]])
f = self.func(self.x[argm])
if self.storeAllEvaluations:
self._allEvaluations.append(f)
if self.storeAllEvaluated:
self._allEvaluated.append(self.x[argm])
self.y = np.hstack([self.y, f])
if f < self.fmin:
self.fmin = f
self.argmin = self.x[argm]
self.time = time.time() - wall_time
return (np.array(self.argmin), self.fmin)
def decompose_model(timeseries, fcst_window):
composed_df = pd.DataFrame()
res_df = pd.DataFrame()
res_test = sm.seasonal_decompose(timeseries.dropna(),
two_sided=False)
composed_df['trend'] = res_test.trend.dropna()
composed_df['seasonal'] = res_test.seasonal.dropna()
composed_df['residual'] = res_test.resid.dropna()
# create date index for the output data frame
date_rng = pd.date_range(composed_df.index[len(composed_df) - 1] + DateOffset(months=1),
periods=fcst_window,
freq='MS')
res_df['Date'] = pd.to_datetime(date_rng,
errors='coerce')
res_df = res_df.sort_values(by='Date')
res_df = res_df.set_index('Date')
# predict the residual component
resid_mean = composed_df['residual'].mean()
res_df['Residual'] = resid_mean
# predict the seasonal component
last_year = date_rng[0].year - 1
last_year_rng = pd.date_range(date(last_year, 1, 1),
periods=12,
freq='MS')
seas_data = composed_df.loc[composed_df.index.isin(last_year_rng)].seasonal
seas_val = list()
for i in range(fcst_window):
seas_val.append(seas_data[res_df.index[i].month - 1])
res_df['Seasonal'] = seas_val
# predict the trend component (Gaussian Process)
x_fit = (composed_df.index - composed_df.index[0]).days.tolist()
x_test = (res_df.index - composed_df.index[0]).days.tolist()
x_fit_np = np.asarray(x_fit).reshape((-1, 1))
x_test_np = np.asarray(x_test).reshape((-1, 1))
y_fit = composed_df['trend'].values
y_fit_np = np.asarray(y_fit).reshape((-1, 1))
gpr = GaussianProcess(corr='cubic', regr='linear', theta0=1e-2, thetaL=1e-4, thetaU=1e-1,
random_start=100)
gpr.fit(x_fit_np, y_fit_np)
y_gpr = gpr.predict(x_test_np)
res_df['Trend'] = y_gpr
res_df['Total'] = res_df.sum(axis=1)
res_df.loc[res_df['Total'] < 0, 'Total'] = 0
return res_df
def __init__(self,
num_in_samples=2,
sampling_function=uniform_random_sampling):
self.gp = GaussianProcess(corr='squared_exponential',
theta0=1e-2,
thetaL=1e-4,
thetaU=1e-1,
random_start=100)
self.results_hash = {}
self.observations = []
self.samples = []
self.num_in_samples = num_in_samples
self.sampling_function = sampling_function
self.model_robot_results = {}
self.generateX_AND_getY_for_training()
def __init__(self,
func,
initial,
minimize=True,
storeAllEvaluations=True,
storeAllEvaluated=True,
maxEvaluations=10):
self.func = func
self.lst = initial
self.gp = GaussianProcess(corr='cubic',
theta0=1e-2,
thetaL=1e-4,
thetaU=1e-1,
random_start=100)
self.max_evaluations = maxEvaluations
def test_batch_size():
# TypeError when using batch_size on Python 3, see
# https://github.com/scikit-learn/scikit-learn/issues/7329 for more
# details
gp = GaussianProcess()
gp.fit(X, y)
gp.predict(X, batch_size=1)
gp.predict(X, batch_size=1, eval_MSE=True)