def get_scalar_matrix(kernel_matrix):
reduce_kernel = lambda kernel: [val
for (freq, val) in zip(self.frequency_range,kernel)
if frequency_filter(freq,freq_bound/3)]
get_scalar = lambda kernel: np.sum(reduce_kernel(kernel))
scalar_matrix = utility.nested_map(get_scalar, kernel_matrix)
return scalar_matrix
def filter_attribute(attribute_centroid_matrix,
list_attribute_keys,
attribute):
attr_index = list_attribute_keys.index(attribute)
attr_matrix = utility.nested_map(lambda L: L[attr_index],
attribute_centroid_matrix,
ignore_diagonal = True)
return attr_matrix
def get_time_scale(attribute_centroid_matrix,
list_attribute_keys):
spectral_scales = filter_attribute(attribute_centroid_matrix,
list_attribute_keys,
'scale')
temporal_scales = utility.nested_map(lambda x: 1./x,
spectral_scales,
ignore_diagonal = True)
return temporal_scales
def __get_covariance_matrices(self):
## private functions
def get_covariance_matrix(parameter_tuple):
stationary_covariance_function = lambda f,l,tau: np.exp(-1j*tau*f-f**2/(2*l**2))
time_scale = parameter_tuple[0]
time_shift = parameter_tuple[1]
spectral_smoothing = parameter_tuple[2]
spectral_width = 2*np.pi*(1/time_scale)
diagonal_covariance_matrix = np.matrix(np.diag(stationary_covariance_function(self.frequency_range,
spectral_width,
time_shift)))
# Nonstationary part
smoothing_covariance_matrix = np.matrix(self.__squared_exponential_covariance(freq_grid_x - freq_grid_y,
spectral_smoothing))
# Final covariance matrix
covariance_matrix = diagonal_covariance_matrix*smoothing_covariance_matrix*diagonal_covariance_matrix.H
return covariance_matrix
# Frequency grid
freq_grid_x, freq_grid_y = np.meshgrid(self.frequency_range, self.frequency_range)
covariance_matrices = utility.nested_map(get_covariance_matrix, self.parameter_matrices, ignore_diagonal=True)
return covariance_matrices
def get_structural_connectivity_matrix(attribute_centroid_matrix, list_attribute_keys):
## private functions ##
def classify_edge(x):
criteria_list = [criterion(x) for criterion in criteria]
if all(criteria_list):
return 1
elif any(criteria_list):
warnings.warn(message = "The connectivity criterion was not decisive")
return 1
else:
return 0
def get_nested_minimum(index, attribute_centroid_matrix):
minimum_value = float("inf")
filled_matrix = utility.fill_diagonal(attribute_centroid_matrix, tuple_size*[minimum_value])
return utility.nested_foldRight(lambda X,y: min(X[index], y),
min,
minimum_value,
filled_matrix)
##
tuple_size = len(list_attribute_keys)
strength_index = list_attribute_keys.index("connectivity_strength")
minimum_strength = get_nested_minimum(strength_index, attribute_centroid_matrix)
strength_criterion = lambda x: x[strength_index]>minimum_strength
effect_size_index = list_attribute_keys.index("effect_size")
minimum_effect_size = get_nested_minimum(effect_size_index, attribute_centroid_matrix)
effect_size_criterion = lambda x: x[effect_size_index]>minimum_effect_size
log_lk_index = list_attribute_keys.index("log_likelihood_ratio")
minimum_log_lk = get_nested_minimum(log_lk_index, attribute_centroid_matrix)
log_lk_criterion = lambda x: x[log_lk_index]>minimum_log_lk
criteria = [strength_criterion, effect_size_criterion, log_lk_criterion]
filled_matrix = utility.fill_diagonal(attribute_centroid_matrix, tuple_size*[-float("inf")])
structural_connectivity_matrix = utility.nested_map(classify_edge, filled_matrix, ignore_diagonal=False)
return structural_connectivity_matrix
def run_analysis(self, data, onlyTrials=False, show_diagnostics=False):
## private functions
def run_analysis_body(sample):
#(nsources, nfrequencies) = np.shape(sample)
sample_connectivity = np.zeros((nsources, nsources, nfrequencies))
x = self.__get_fourier_time_series(sample)
Px = self.__get_modified_processes(x, dynamic_polynomials)
observation_models = self.__get_observation_models(x, moving_average_kernels)
for j in range(0, nsources): # target
total_covariance_matrix = self.__get_total_covariance_matrix(covariance_matrices,
observation_models,
j)
for i in range(0, nsources): # source
if self.structural_constraint[i,j]:
connectivity_kernel = self.__posterior_kernel_cij_temporal(observation_models[i],
covariance_matrices[i][j], # check this! [i,j] or [j,i]??
total_covariance_matrix,
Px[j])
sample_connectivity[i, j, :] = connectivity_kernel
return sample_connectivity
#
def run_analysis_serial(data):
connectivity = np.zeros((nsamples, nsources, nsources, nfrequencies))
for s_ix, sample in enumerate(data):
connectivity[s_ix, :, :, :] = run_analysis_body(sample)
return connectivity
#
def run_analysis_parallel(data):
## private function
def run_analysis_parallel_wrapper(self, parallel_args_struct):
return self.__run_analysis_body(sample=parallel_args_struct['sample'],
moving_average_kernels=parallel_args_struct['MA'],
covariance_matrix=parallel_args_struct['cov'],
dynamic_polynomials=parallel_args_struct['dypoly'])
# function body
connectivity = np.zeros((nsamples, nsources, nsources, nfrequencies))
# Initialize parallelization
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(processes=self.parallelthreads)
parallel_args = []
for sample in data: #?
parallel_args_struct = {}
parallel_args_struct['sample'] = sample
parallel_args_struct['MA'] = moving_average_kernels
parallel_args_struct['cov'] = covariance_matrices
parallel_args_struct['dypoly'] = dynamic_polynomials
parallel_args += [parallel_args_struct]
# Execute parallel computation
parallel_results_list = pool.map(run_analysis_parallel_wrapper, parallel_args)
# Collect results
for i in range(0, nsamples):
connectivity[i,:,:,:] = parallel_results_list[i]
pool.close()
pool.join()
return connectivity
#
def run_analysis_parallel_flatloop(data):
## private function
def posterior_kernel_cij_temporal_parwrap(parallel_args_struct):
"""
The bread-and-butter of the method.
"""
# unpack
y = np.matrix(parallel_args_struct['Px_j']).T
covariance_matrices = parallel_args_struct['cov']
observation_models = parallel_args_struct['obs_models']
i = parallel_args_struct['i']
j = parallel_args_struct['j']
total_cov_matrix = self.__get_total_covariance_matrix(covariance_matrices,
observation_models,
j,
self.noise_level)
c_ij = covariance_matrices * observation_models[i].H \
* matrix_division(divider = total_cov_matrix, divided = y, side = "left", cholesky = "no")
return np.real(np.fft.fftshift(np.fft.ifft(np.fft.ifftshift(np.array(c_ij).flatten()), axis = 0)))
# body
connectivity = np.zeros((nsamples, nsources, nsources, nfrequencies))
# Initialize parallelization
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(processes=self.parallelthreads)
parallel_iterable = []
for k, sample in enumerate(data): #?
x = self.__get_fourier_time_series(sample)
Px = self.__get_modified_processes(x, dynamic_polynomials)
observation_models = self.__get_observation_models(x, moving_average_kernels)
for j in range(0, nsources):
for i in range(0, nsources):
if self.structural_constraint[i,j]:
parallel_args_struct = {}
parallel_args_struct['cov'] = covariance_matrices[i]
parallel_args_struct['k'] = k
parallel_args_struct['obs_models'] = observation_models
parallel_args_struct['i'] = i
parallel_args_struct['j'] = j
parallel_args_struct['Px_j'] = Px[j]
parallel_iterable += [parallel_args_struct]
# Execute parallel computation
parallel_results_list = pool.map(posterior_kernel_cij_temporal_parwrap, parallel_iterable)
# todo: properly unwrap this, instead of this ugly hack:
for m, result in enumerate(parallel_results_list):
k = parallel_iterable[m]['k']
i = parallel_iterable[m]['i']
j = parallel_iterable[m]['j']
connectivity[k,i,j,:] = result
return connectivity
## function body of run_analysis()
dynamic_polynomials = self.__get_dynamic_polynomials()
moving_average_kernels = self.__get_moving_average_kernels()
covariance_matrices = self.__get_covariance_matrices()
(nsamples, nsources, nfrequencies) = np.shape(np.asarray(data))
self.__get_frequency_range()
if self.structural_constraint is None:
self.structural_constraint = np.ones(shape=(nsources, nsources)) - np.eye(nsources)
if show_diagnostics:
print 'GP CaKe parameters:'
print '\nTime scales (nu_ij):'
print np.array(utility.fill_diagonal(utility.nested_map(lambda x: x[0], self.parameter_matrices), 0))
print '\nTime shifts (t_ij):'
print np.array(utility.fill_diagonal(utility.nested_map(lambda x: x[1], self.parameter_matrices), 0))
print '\nSpectral smoothing: (theta_ij)'
print np.array(utility.fill_diagonal(utility.nested_map(lambda x: x[2], self.parameter_matrices), 0))
print '\nNoise levels (sigma_i):'
print np.array(self.noise_vector)
print '\nConnectivity constraint (G_ij):'
print self.structural_constraint
utility.tic()
connectivity = None
if self.parallelthreads > 1 and not onlyTrials:
print('Parallel implementation (p = {:d}).'.format(self.parallelthreads))
connectivity = run_analysis_parallel_flatloop(data)
elif self.parallelthreads > 1 and onlyTrials:
print('Parallel implementation (p = {:d}, over trials only).'.format(self.parallelthreads))
connectivity = run_analysis_parallel(data)
else:
#print('Serial implementation.')
connectivity = run_analysis_serial(data)
if show_diagnostics:
utility.toc()
return connectivity
def get_attribute_matrices(result_matrices):
##
def fit_gaussian(empirical_kernel,
grid_size = 6000):
## private lambdas ##
unzip = lambda lst: zip(*lst)
deviation_function = lambda x, y: np.sum(np.abs(x - y))
shift = lambda x: x - np.median(np.sort(x)[:5])
normalize = lambda x, dx: x/(np.sum(x) * dx)
smoothing_function = lambda x, l: np.exp(-np.power(x,2)/(2*l**2))/(np.sqrt(np.pi*2*l**2))
## function body ##
df = np.median(np.diff(self.frequency_range))
if len(empirical_kernel) == 0:
return []
else:
frequencies, values = unzip([(freq, val)
for (freq,val) in zip(self.frequency_range, empirical_kernel)
if frequency_filter(freq, freq_bound)])
scale_range = np.linspace(10**-4,2*freq_bound,grid_size)
deviations = [deviation_function(normalize(shift(values), df), smoothing_function(frequencies,l))
for l in scale_range]
optimal_scale = scale_range[np.argmin(deviations)]
return optimal_scale
def get_scalar_matrix(kernel_matrix):
reduce_kernel = lambda kernel: [val
for (freq, val) in zip(self.frequency_range,kernel)
if frequency_filter(freq,freq_bound/3)]
get_scalar = lambda kernel: np.sum(reduce_kernel(kernel))
scalar_matrix = utility.nested_map(get_scalar, kernel_matrix)
return scalar_matrix
symmetrize = lambda A: A + np.transpose(A)
##
strength_matrix = np.array(get_scalar_matrix(result_matrices["ls_second_moment"]))
np.fill_diagonal(strength_matrix, 0.5)
residual_matrix = np.array(get_scalar_matrix(result_matrices["corrected_residual_variance"]))
np.fill_diagonal(residual_matrix, 1)
empirical_ls_estimate_matrix = np.array(get_scalar_matrix(utility.nested_map(lambda x: np.power(np.abs(x),2),
result_matrices["ls_estimate"],
ignore_diagonal=True)))
np.fill_diagonal(empirical_ls_estimate_matrix, 0)
effect_size_matrix = np.divide(empirical_ls_estimate_matrix,residual_matrix)
log_likelihood_matrix = np.array(get_scalar_matrix(result_matrices["log_likelihood_ratio"]))
np.fill_diagonal(log_likelihood_matrix, 0.)
scale_matrix = np.array(utility.nested_map(fit_gaussian,
result_matrices["ls_second_moment"],
ignore_diagonal=True))
np.fill_diagonal(scale_matrix, 0)
if show_diagnostics:
diagnostics.plot_scale_fit(diagnostics_output_prefix,
result_matrices["ls_second_moment"],
scale_matrix,
self.frequency_range,
freq_bound
)
symmetric_scale_matrix = (symmetrize(strength_matrix*scale_matrix)/(symmetrize(strength_matrix)))
return {"scale": symmetric_scale_matrix.tolist(),
"connectivity_strength": strength_matrix.tolist(),
"log_likelihood_ratio": log_likelihood_matrix.tolist(),
"effect_size": effect_size_matrix.tolist(),
"residuals": residual_matrix.tolist()
}
def empirical_bayes_parameter_fit(self, time_domain_trials, learn_structural_constraint=False, show_diagnostics=False, diagnostics_output_prefix='diagnostics_', freq_bound_fraction=1):
## private function ##
def rearrange_results(least_squares_results, field):
##
def correct_index(in_index,out_index):
if in_index < out_index:
return in_index
else:
return in_index - 1
##
kernel_list = []
for out_index in range(0, number_sources):
kernel_list_row = []
for in_index in range(0, number_sources):
if out_index == in_index:
kernel_array = []
else:
kernel = []
corr_in_index = correct_index(in_index, out_index)
for frequency_index in range(0, number_frequencies):
input_results = least_squares_results[out_index][frequency_index][field]
if type(input_results) is np.float64:
kernel += [np.real(input_results)]
else:
kernel += [np.real(input_results[corr_in_index])]
kernel_array = np.array(kernel)
kernel_list_row += [kernel_array]
kernel_list += [kernel_list_row]
return kernel_list
#
def get_attribute_matrices(result_matrices):
##
def fit_gaussian(empirical_kernel,
grid_size = 6000):
## private lambdas ##
unzip = lambda lst: zip(*lst)
deviation_function = lambda x, y: np.sum(np.abs(x - y))
shift = lambda x: x - np.median(np.sort(x)[:5])
normalize = lambda x, dx: x/(np.sum(x) * dx)
smoothing_function = lambda x, l: np.exp(-np.power(x,2)/(2*l**2))/(np.sqrt(np.pi*2*l**2))
## function body ##
df = np.median(np.diff(self.frequency_range))
if len(empirical_kernel) == 0:
return []
else:
frequencies, values = unzip([(freq, val)
for (freq,val) in zip(self.frequency_range, empirical_kernel)
if frequency_filter(freq, freq_bound)])
scale_range = np.linspace(10**-4,2*freq_bound,grid_size)
deviations = [deviation_function(normalize(shift(values), df), smoothing_function(frequencies,l))
for l in scale_range]
optimal_scale = scale_range[np.argmin(deviations)]
return optimal_scale
def get_scalar_matrix(kernel_matrix):
reduce_kernel = lambda kernel: [val
for (freq, val) in zip(self.frequency_range,kernel)
if frequency_filter(freq,freq_bound/3)]
get_scalar = lambda kernel: np.sum(reduce_kernel(kernel))
scalar_matrix = utility.nested_map(get_scalar, kernel_matrix)
return scalar_matrix
symmetrize = lambda A: A + np.transpose(A)
##
strength_matrix = np.array(get_scalar_matrix(result_matrices["ls_second_moment"]))
np.fill_diagonal(strength_matrix, 0.5)
residual_matrix = np.array(get_scalar_matrix(result_matrices["corrected_residual_variance"]))
np.fill_diagonal(residual_matrix, 1)
empirical_ls_estimate_matrix = np.array(get_scalar_matrix(utility.nested_map(lambda x: np.power(np.abs(x),2),
result_matrices["ls_estimate"],
ignore_diagonal=True)))
np.fill_diagonal(empirical_ls_estimate_matrix, 0)
effect_size_matrix = np.divide(empirical_ls_estimate_matrix,residual_matrix)
log_likelihood_matrix = np.array(get_scalar_matrix(result_matrices["log_likelihood_ratio"]))
np.fill_diagonal(log_likelihood_matrix, 0.)
scale_matrix = np.array(utility.nested_map(fit_gaussian,
result_matrices["ls_second_moment"],
ignore_diagonal=True))
np.fill_diagonal(scale_matrix, 0)
if show_diagnostics:
diagnostics.plot_scale_fit(diagnostics_output_prefix,
result_matrices["ls_second_moment"],
scale_matrix,
self.frequency_range,
freq_bound
)
symmetric_scale_matrix = (symmetrize(strength_matrix*scale_matrix)/(symmetrize(strength_matrix)))
return {"scale": symmetric_scale_matrix.tolist(),
"connectivity_strength": strength_matrix.tolist(),
"log_likelihood_ratio": log_likelihood_matrix.tolist(),
"effect_size": effect_size_matrix.tolist(),
"residuals": residual_matrix.tolist()
}
#
def get_attribute_centroid_matrix(dic_attribute_matrices):
## private functions ##
def replace_with_centroid(centroids,
attributes_std,
list_attribute_matrices):
rescale = lambda x: np.divide(x,attributes_std)
distance = lambda x,y: np.linalg.norm(np.array(x)-np.array(y))
closest_centroid = lambda x: np.multiply(centroids[np.argmin([distance(rescale(x),c)
for c in centroids]),:],attributes_std)
quantized_matrix = [map(closest_centroid, row)
for row in utility.nested_zip(*list_attribute_matrices)]
quantized_matrix = utility.fill_diagonal(quantized_matrix, [])
return quantized_matrix
flat_list = lambda lst: [item
for index1, sublist in enumerate(lst)
for index2, item in enumerate(sublist)
if index1 != index2]
def standardize(data_array):
standardized_array = np.zeros(data_array.shape)
attributes_std = np.array([np.std(row) for row in data_array])
for attribute_index, scale_factor in enumerate(attributes_std):
if scale_factor != 0:
standardized_array[attribute_index,:] = data_array[attribute_index,:] / scale_factor
else:
standardized_array[attribute_index,:] = data_array[attribute_index,:]
return (standardized_array, attributes_std)
#
## method body ##
list_attribute_keys = dic_attribute_matrices.keys()
list_attribute_matrices = dic_attribute_matrices.values()
flat_matrices = map(flat_list, list_attribute_matrices)
print(list_attribute_keys[1])
print(np.array(flat_matrices))
standardized_data, attributes_std = standardize(np.array(flat_matrices))
standardized_centroids,_ = cluster.vq.kmeans(np.transpose(standardized_data), 2)
print(standardized_data)
attribute_centroid_matrix = replace_with_centroid(standardized_centroids,
attributes_std,
list_attribute_matrices)
if show_diagnostics:
diagnostics.plot_distances(diagnostics_output_prefix, standardized_data, standardized_centroids)
return (attribute_centroid_matrix, list_attribute_keys)
#
def get_structural_connectivity_matrix(attribute_centroid_matrix, list_attribute_keys):
## private functions ##
def classify_edge(x):
criteria_list = [criterion(x) for criterion in criteria]
if all(criteria_list):
return 1
elif any(criteria_list):
warnings.warn(message = "The connectivity criterion was not decisive")
return 1
else:
return 0
def get_nested_minimum(index, attribute_centroid_matrix):
minimum_value = float("inf")
filled_matrix = utility.fill_diagonal(attribute_centroid_matrix, tuple_size*[minimum_value])
return utility.nested_foldRight(lambda X,y: min(X[index], y),
min,
minimum_value,
filled_matrix)
##
tuple_size = len(list_attribute_keys)
strength_index = list_attribute_keys.index("connectivity_strength")
minimum_strength = get_nested_minimum(strength_index, attribute_centroid_matrix)
strength_criterion = lambda x: x[strength_index]>minimum_strength
effect_size_index = list_attribute_keys.index("effect_size")
minimum_effect_size = get_nested_minimum(effect_size_index, attribute_centroid_matrix)
effect_size_criterion = lambda x: x[effect_size_index]>minimum_effect_size
log_lk_index = list_attribute_keys.index("log_likelihood_ratio")
minimum_log_lk = get_nested_minimum(log_lk_index, attribute_centroid_matrix)
log_lk_criterion = lambda x: x[log_lk_index]>minimum_log_lk
criteria = [strength_criterion, effect_size_criterion, log_lk_criterion]
filled_matrix = utility.fill_diagonal(attribute_centroid_matrix, tuple_size*[-float("inf")])
structural_connectivity_matrix = utility.nested_map(classify_edge, filled_matrix, ignore_diagonal=False)
return structural_connectivity_matrix
#
def filter_attribute(attribute_centroid_matrix,
list_attribute_keys,
attribute):
attr_index = list_attribute_keys.index(attribute)
attr_matrix = utility.nested_map(lambda L: L[attr_index],
attribute_centroid_matrix,
ignore_diagonal = True)
return attr_matrix
#
def get_time_scale(attribute_centroid_matrix,
list_attribute_keys):
spectral_scales = filter_attribute(attribute_centroid_matrix,
list_attribute_keys,
'scale')
temporal_scales = utility.nested_map(lambda x: 1./x,
spectral_scales,
ignore_diagonal = True)
return temporal_scales
#
def get_noise_level(attribute_centroid_matrix,
list_attribute_keys):
noise_levels = filter_attribute(attribute_centroid_matrix,
list_attribute_keys,
'residuals')
return noise_levels
#
frequency_filter = lambda freq, freq_bound: ((freq > -freq_bound) & (freq < freq_bound))
## function body ##
print 'Training GP CaKe parameters with empirical Bayes.'
number_sources, number_frequencies = time_domain_trials[0].shape
freq_bound = np.max(np.abs(self.frequency_range)) / float(freq_bound_fraction)
##
ls_results = self.empirical_least_squares_analysis(time_domain_trials)
fields_list = ["ls_second_moment", "log_likelihood_ratio", "corrected_residual_variance", "ls_estimate"]
result_matrices = {field: rearrange_results(ls_results, field)
for field in fields_list}
dic_attribute_matrices = get_attribute_matrices(result_matrices)
attribute_centroid_matrix, list_attribute_keys = get_attribute_centroid_matrix(dic_attribute_matrices)
time_scale = get_time_scale(attribute_centroid_matrix,
list_attribute_keys)
scale2shift_proportion = 1.
time_shift = utility.nested_map(lambda x: scale2shift_proportion*x, time_scale, ignore_diagonal=True)
noise_level = get_noise_level(attribute_centroid_matrix,
list_attribute_keys)
noise_level = utility.fill_diagonal(noise_level, float('nan'))
noise_level_vector = map(lambda noise_list: np.nanmean(noise_list), noise_level)
scale2spectral_proportion = 100.
spectral_smoothing = utility.nested_map(lambda x: scale2spectral_proportion*x, time_scale, ignore_diagonal=True)
parameter_matrices = utility.nested_zip(time_scale,
time_shift,
spectral_smoothing)
self.parameter_matrices = parameter_matrices
self.noise_vector = noise_level_vector
if learn_structural_constraint:
structural_connectivity_matrix = get_structural_connectivity_matrix(attribute_centroid_matrix, list_attribute_keys)
self.structural_constraint = np.array(structural_connectivity_matrix)
print 'Connectivity constraint: enabled.'
else:
print 'Connectivity constraint: disabled.'
print 'Empirical Bayes procedure complete.'