def fit(self, ys, paradigm, initial_beta=None):
rng = check_random_state(self.random_state)
ys = np.atleast_1d(ys)
if self.normalize_y:
self.y_train_mean = np.mean(ys, axis=0)
ys = ys - self.y_train_mean
else:
self.y_train_mean = np.zeros(1)
# Removing the drifts
if self.remove_difts:
names, onsets, durations, modulation = check_paradigm(paradigm)
frame_times = np.arange(0, onsets.max() + self.time_offset, self.t_r)
drifts, dnames = _make_drift(self.drift_model, frame_times,
self.order, self.period_cut)
ys -= drifts.dot(np.linalg.pinv(drifts).dot(ys))
if self.zeros_extremes:
if ys.ndim==2:
ys = np.append(ys, np.zeros((2, ys.shape[1])), axis=0)
else:
ys = np.append(ys, np.zeros((2,)), axis=0)
self.y_train = ys
# Get paradigm data
hrf_measurement_points, visible_events, etas, beta_indices, unique_events = \
_get_hrf_measurements(paradigm, hrf_length=self.hrf_length,
t_r=self.t_r, time_offset=self.time_offset,
zeros_extremes=self.zeros_extremes,
frame_times=frame_times)
if initial_beta is None:
initial_beta = np.ones(len(unique_events))
# Just to be able to use Kernels class
hrf_measurement_points = np.concatenate(hrf_measurement_points)
self.hrf_measurement_points = hrf_measurement_points[:, np.newaxis]
self.evaluation_points = None
etas = np.concatenate(etas)
# Initialize the kernel
self.hrf_kernel = HRFKernel(kernel=self.kernel, gamma=self.gamma,
return_eval_cov=self.return_var)
self.hrf_kernel.set_params(**dict(
beta_values=initial_beta, beta_indices=beta_indices, etas=etas))
self.visible_events_ = visible_events
self.unique_events_ = unique_events
self.etas_ = etas
self.beta_indices_ = beta_indices
self.initial_beta_ = initial_beta
# Maximizing the log-likelihood (gradient based optimization)
self.f_mean_ = self.f_mean
self.f_mean = None
if self.optimize:
def obj_func(theta):
print theta
return -self._fit(theta)[0]
optima = [(self._constrained_optimization(
obj_func, self.hrf_kernel.theta, self.hrf_kernel.bounds))]
# Additional runs are performed from log-uniform chosen initial
# theta
if self.n_restarts_optimizer > 0:
bounds = self.hrf_kernel.bounds
for i in range(self.n_restarts_optimizer):
theta_initial = rng.uniform(bounds[:, 0], bounds[:, 1])
optima.append(self._constrained_optimization(
obj_func, theta_initial, bounds))
# Select the best result
# add logic to deal with nan and -inf
lm_values = list(map(itemgetter(1), optima))
self.theta_ = optima[np.argmin(lm_values)][0]
self.hrf_kernel.theta = self.theta_
self.log_marginal_likelihood_value_ = -np.min(lm_values)
# Refit the model
self.f_mean = self.f_mean_
loglikelihood, output = self._fit(self.theta_)
else:
loglikelihood, output = self._fit(self.hrf_kernel.theta)
hrf_measurement_points = np.concatenate(output[1][0])
order = np.argsort(hrf_measurement_points)
hrf_var = output[1][2][order]
hx, hy = hrf_measurement_points[order], output[1][1][order]
residual_norm_squared = output[2][0]
sigma_squared_resid = output[2][1]
self.beta = output[0]
self.hx_ = hx
self.hrf_ = hy
self.hrf_var_ = hrf_var
return (hx, hy, hrf_var, residual_norm_squared, sigma_squared_resid)
def make_design_matrix_hrf(
frame_times, paradigm=None, hrf_length=32., t_r=2., time_offset=10,
drift_model='cosine', period_cut=128, drift_order=1, fir_delays=[0],
add_regs=None, add_reg_names=None, min_onset=-24, f_hrf=None):
"""Generate a design matrix from the input parameters
Parameters
----------
frame_times : array of shape (n_frames,)
The timing of the scans in seconds.
paradigm : DataFrame instance, optional
Description of the experimental paradigm.
drift_model : string, optional
Specifies the desired drift model,
It can be 'polynomial', 'cosine' or 'blank'.
period_cut : float, optional
Cut period of the low-pass filter in seconds.
drift_order : int, optional
Order of the drift model (in case it is polynomial).
fir_delays : array of shape(n_onsets) or list, optional,
In case of FIR design, yields the array of delays used in the FIR
model.
add_regs : array of shape(n_frames, n_add_reg), optional
additional user-supplied regressors
add_reg_names : list of (n_add_reg,) strings, optional
If None, while n_add_reg > 0, these will be termed
'reg_%i', i = 0..n_add_reg - 1
min_onset : float, optional
Minimal onset relative to frame_times[0] (in seconds)
events that start before frame_times[0] + min_onset are not considered.
Returns
-------
design_matrix : DataFrame instance,
holding the computed design matrix
"""
# check arguments
# check that additional regressor specification is correct
n_add_regs = 0
if add_regs is not None:
if add_regs.shape[0] == np.size(add_regs):
add_regs = np.reshape(add_regs, (np.size(add_regs), 1))
n_add_regs = add_regs.shape[1]
assert add_regs.shape[0] == np.size(frame_times), ValueError(
'incorrect specification of additional regressors: '
'length of regressors provided: %s, number of ' +
'time-frames: %s' % (add_regs.shape[0], np.size(frame_times)))
# check that additional regressor names are well specified
if add_reg_names is None:
add_reg_names = ['reg%d' % k for k in range(n_add_regs)]
elif len(add_reg_names) != n_add_regs:
raise ValueError(
'Incorrect number of additional regressor names was provided'
'(%s provided, %s expected) % (len(add_reg_names),'
'n_add_regs)')
# computation of the matrix
names = []
matrix = None
# step 1: paradigm-related regressors
if paradigm is not None:
# create the condition-related regressors
names0, _, _, _ = check_paradigm(paradigm)
names = np.append(names, np.unique(names0))
hrf_measurement_points, _, _, beta_indices, _ = \
_get_hrf_measurements(paradigm, hrf_length=hrf_length,
t_r=t_r, time_offset=time_offset, frame_times=frame_times)
hrf_measurement_points = np.concatenate(hrf_measurement_points)
hrf_measures = f_hrf(hrf_measurement_points)
matrix = _get_design_from_hrf_measures(hrf_measures, beta_indices)
#matrix, names = _convolve_regressors(
# paradigm, hrf_model.lower(), frame_times, fir_delays, min_onset)
# step 2: additional regressors
if add_regs is not None:
# add user-supplied regressors and corresponding names
if matrix is not None:
matrix = np.hstack((matrix, add_regs))
else:
matrix = add_regs
names = np.append(names, add_reg_names)
# step 3: drifts
drift, dnames = _make_drift(drift_model.lower(), frame_times, drift_order,
period_cut)
if matrix is not None:
matrix = np.hstack((matrix, drift))
else:
matrix = drift
names = np.append(names, dnames)
# step 4: Force the design matrix to be full rank at working precision
matrix, _ = full_rank(matrix)
design_matrix = pd.DataFrame(
matrix, columns=list(names), index=frame_times)
return design_matrix
# Paradigm file
paradigm_fn = op.join(folder0, 'onsets.csv')
########################################################################
# Load data and parameters
t_r = 3.
ys_ = np.load(voxel_fn)
ysr2_ = np.load(voxel_r2_fn)
n_scans = ys.shape[0]
# Create design matrix
frametimes = np.arange(0, n_scans * t_r, t_r)
paradigm = experimental_paradigm.paradigm_from_csv(paradigm_fn)
dm = design_matrix.make_design_matrix(frametimes, paradigm=paradigm)
modulation = np.array(paradigm)[:, 4]
drifts = _make_drift('cosine', frame_times)
ys = ys_ - drifts.dot(np.linalg.pinv(drifts).dot(ys_))
ysr2 = ysr2_ - drifts.dot(np.linalg.pinv(drifts).dot(ysr2_))
# GP parameters
time_offset = 6
gamma = 4
fmin_max_iter = 50
n_restarts_optimizer = 10
n_iter = 10
normalize_y = False
optimize = False
zeros_extremes = True
# Estimation HRF on 1 run
gp = SuperDuperGP(hrf_length=hrf_length, t_r=t_r, oversampling=1./dt,
