def test_bootstrap_sample(self):
from rsatoolbox.inference import bootstrap_sample
from rsatoolbox.rdm import RDMs
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
rdm_sample = bootstrap_sample(rdms)
assert rdm_sample[0].n_cond == 5
assert rdm_sample[0].n_rdm == 11
def test_bootstrap_sample_descriptors(self):
from rsatoolbox.inference import bootstrap_sample
from rsatoolbox.rdm import RDMs
dis = np.random.rand(11, 10) # 11 5x5 rdms
mes = "Euclidean"
des = {'subj': 0}
rdm_des = {'session': np.array([1, 1, 2, 2, 4, 5, 6, 7, 7, 7, 7])}
pattern_des = {'type': np.array([0, 1, 2, 3, 4])}
rdms = RDMs(dissimilarities=dis,
rdm_descriptors=rdm_des,
pattern_descriptors=pattern_des,
dissimilarity_measure=mes,
descriptors=des)
rdm_sample = bootstrap_sample(rdms, 'session', 'type')
assert rdm_sample[0].n_cond == 5
def bootstrap_crossval(models,
data,
method='cosine',
fitter=None,
k_pattern=None,
k_rdm=None,
N=1000,
n_cv=2,
pattern_descriptor='index',
rdm_descriptor='index',
random=True,
boot_type='both',
use_correction=True):
"""evaluates a set of models by k-fold crossvalidation within a bootstrap
Crossvalidation creates variance in the results for a single bootstrap
sample, because different assginments to the training and test group
lead to different results. To correct for this, we apply a formula
which estimates the variance we expect if we evaluated all possible
crossvalidation assignments from n_cv different assignments per bootstrap
sample.
In our statistical evaluations we saw that many bootstrap samples and
few different crossvalidation assignments are optimal to minimize the
variance of the variance estimate. Thus, this function by default
applies this correction formula and sets n_cv=2, i.e. performs only two
different assignments per fold.
This function nonetheless performs full crossvalidation schemes, i.e.
in every bootstrap sample all crossvalidation folds are evaluated such
that each RDM and each condition is in the test set n_cv times. For the
even more optimized version which computes only two randomly chosen test
sets see bootstrap_cv_random.
The k_[] parameters control the cross-validation per sample. They give
the number of crossvalidation folds to be created along this dimension.
If a k is set to 1 no crossvalidation is performed over the
corresponding dimension.
by default ks are set by rsatoolbox.util.inference_util.default_k_pattern
and rsatoolbox.util.inference_util.default_k_rdm based on the number of
rdms and patterns provided. the ks are then in the range 2-5.
Using the []_descriptor inputs you may make the crossvalidation and
bootstrap aware of groups of rdms or conditions to be handled en block.
Conditions with the same entry will be sampled in or out of the bootstrap
together and will be assigned to cross-calidation folds together.
Using the boot_type argument you may choose the dimension to bootstrap.
By default both conditions and RDMs are resampled. You may alternatively
choose to resample only one of them by passing 'rdm' or 'pattern'.
models should be a list of models. data the RDMs object to evaluate against
method the method for comparing the predictions and the data. fitter may
provide a non-default funcion or list of functions to fit the models.
Args:
models(rsatoolbox.model.Model): models to be evaluated
data(rsatoolbox.rdm.RDMs): RDM data to use
method(string): comparison method to use
fitter(function): fitting method for models
k_pattern(int): #folds over patterns
k_rdm(int): #folds over rdms
N(int): number of bootstrap samples (default: 1000)
n_cv(int) : number of crossvalidation runs per sample (default: 1)
pattern_descriptor(string): descriptor to group patterns
rdm_descriptor(string): descriptor to group rdms
random(bool): randomize group assignments (default: True)
boot_type(String): which dimension to bootstrap over (default: 'both')
alternatives: 'rdm', 'pattern'
use_correction(bool): switch for the correction for the
variance caused by crossvalidation (default: True)
Returns:
numpy.ndarray: matrix of evaluations (N x k)
"""
if k_pattern is None:
n_pattern = len(np.unique(
data.pattern_descriptors[pattern_descriptor]))
k_pattern = default_k_pattern((1 - 1 / np.exp(1)) * n_pattern)
if k_rdm is None:
n_rdm = len(np.unique(data.rdm_descriptors[rdm_descriptor]))
k_rdm = default_k_rdm((1 - 1 / np.exp(1)) * n_rdm)
if isinstance(models, Model):
models = [models]
evaluations = np.empty((N, len(models), k_pattern * k_rdm, n_cv))
noise_ceil = np.empty((2, N, n_cv))
for i_sample in tqdm.trange(N):
if boot_type == 'both':
sample, rdm_idx, pattern_idx = bootstrap_sample(
data,
rdm_descriptor=rdm_descriptor,
pattern_descriptor=pattern_descriptor)
elif boot_type == 'pattern':
sample, pattern_idx = bootstrap_sample_pattern(
data, pattern_descriptor=pattern_descriptor)
rdm_idx = np.unique(data.rdm_descriptors[rdm_descriptor])
elif boot_type == 'rdm':
sample, rdm_idx = bootstrap_sample_rdm(
data, rdm_descriptor=rdm_descriptor)
pattern_idx = np.unique(
data.pattern_descriptors[pattern_descriptor])
else:
raise ValueError('boot_type not understood')
if len(np.unique(rdm_idx)) >= k_rdm \
and len(np.unique(pattern_idx)) >= 3 * k_pattern:
for i_rep in range(n_cv):
evals, cv_nc = _internal_cv(models, sample, pattern_descriptor,
rdm_descriptor, pattern_idx,
k_pattern, k_rdm, method, fitter)
noise_ceil[:, i_sample, i_rep] = cv_nc
evaluations[i_sample, :, :, i_rep] = evals[0]
else: # sample does not allow desired crossvalidation
evaluations[i_sample, :, :] = np.nan
noise_ceil[:, i_sample] = np.nan
if boot_type == 'both':
cv_method = 'bootstrap_crossval'
dof = min(data.n_rdm, data.n_cond) - 1
elif boot_type == 'pattern':
cv_method = 'bootstrap_crossval_pattern'
dof = data.n_cond - 1
elif boot_type == 'rdm':
cv_method = 'bootstrap_crossval_rdm'
dof = data.n_rdm - 1
eval_ok = ~np.isnan(evaluations[:, 0, 0, 0])
if use_correction and n_cv > 1:
# we essentially project from the two points for 1 repetition and
# for n_cv repetitions to infinitely many cv repetitions
evals_mean = np.mean(np.mean(evaluations[eval_ok], -1), -1)
evals_1 = np.mean(evaluations[eval_ok], -2)
noise_ceil_mean = np.mean(noise_ceil[:, eval_ok], -1)
noise_ceil_1 = noise_ceil[:, eval_ok]
var_mean = np.cov(np.concatenate([evals_mean.T, noise_ceil_mean]))
var_1 = []
for i in range(n_cv):
var_1.append(
np.cov(
np.concatenate([evals_1[:, :, i].T, noise_ceil_1[:, :,
i]])))
var_1 = np.mean(np.array(var_1), axis=0)
# this is the main formula for the correction:
variances = (n_cv * var_mean - var_1) / (n_cv - 1)
else:
if use_correction:
raise Warning('correction requested, but only one cv run' +
' per sample requested. This is invalid!' +
' We do not use the correction for now.')
evals_nonan = np.mean(np.mean(evaluations[eval_ok], -1), -1)
noise_ceil_nonan = np.mean(noise_ceil[:, eval_ok], -1)
variances = np.cov(np.concatenate([evals_nonan.T, noise_ceil_nonan]))
result = Result(models,
evaluations,
method=method,
cv_method=cv_method,
noise_ceiling=noise_ceil,
variances=variances,
dof=dof)
return result
def bootstrap_cv_random(models,
data,
method='cosine',
fitter=None,
n_pattern=None,
n_rdm=None,
N=1000,
n_cv=2,
pattern_descriptor='index',
rdm_descriptor='index',
random=True,
boot_type='both',
use_correction=True):
"""evaluates a set of models by a evaluating a few random crossvalidation
folds per bootstrap.
If a k is set to 1 no crossvalidation is performed over the
corresponding dimension.
As especially crossvalidation over patterns/conditions creates
variance in the cv result for a single variance the default setting
of n_cv=1 inflates the estimated variance. Setting this value
higher will decrease this effect at the cost of more computation time.
by default ks are set by rsatoolbox.util.inference_util.default_k_pattern
and rsatoolbox.util.inference_util.default_k_rdm based on the number of
rdms and patterns provided. the ks are then in the range 2-5.
Args:
models(rsatoolbox.model.Model): models to be evaluated
data(rsatoolbox.rdm.RDMs): RDM data to use
method(string): comparison method to use
fitter(function): fitting method for models
k_pattern(int): #folds over patterns
k_rdm(int): #folds over rdms
N(int): number of bootstrap samples (default: 1000)
n_cv(int) : number of crossvalidation runs per sample (default: 1)
pattern_descriptor(string): descriptor to group patterns
rdm_descriptor(string): descriptor to group rdms
random(bool): randomize group assignments (default: True)
boot_type(String): which dimension to bootstrap over (default: 'both')
alternatives: 'rdm', 'pattern'
use_correction(bool): switch for the correction for the
variance caused by crossvalidation (default: True)
Returns:
numpy.ndarray: matrix of evaluations (N x k)
"""
if n_pattern is None:
n_pattern_all = len(
np.unique(data.pattern_descriptors[pattern_descriptor]))
k_pattern = default_k_pattern((1 - 1 / np.exp(1)) * n_pattern_all)
n_pattern = int(np.floor(n_pattern_all / k_pattern))
if n_rdm is None:
n_rdm_all = len(np.unique(data.rdm_descriptors[rdm_descriptor]))
k_rdm = default_k_rdm((1 - 1 / np.exp(1)) * n_rdm_all)
n_rdm = int(np.floor(n_rdm_all / k_rdm))
if isinstance(models, Model):
models = [models]
evaluations = np.zeros((N, len(models), n_cv))
noise_ceil = np.zeros((2, N, n_cv))
for i_sample in tqdm.trange(N):
if boot_type == 'both':
sample, rdm_idx, pattern_idx = bootstrap_sample(
data,
rdm_descriptor=rdm_descriptor,
pattern_descriptor=pattern_descriptor)
elif boot_type == 'pattern':
sample, pattern_idx = bootstrap_sample_pattern(
data, pattern_descriptor=pattern_descriptor)
rdm_idx = np.unique(data.rdm_descriptors[rdm_descriptor])
elif boot_type == 'rdm':
sample, rdm_idx = bootstrap_sample_rdm(
data, rdm_descriptor=rdm_descriptor)
pattern_idx = np.unique(
data.pattern_descriptors[pattern_descriptor])
else:
raise ValueError('boot_type not understood')
if len(np.unique(rdm_idx)) > n_rdm \
and len(np.unique(pattern_idx)) >= 3 + n_pattern:
train_set, test_set, ceil_set = sets_random(
sample,
pattern_descriptor=pattern_descriptor,
rdm_descriptor=rdm_descriptor,
n_pattern=n_pattern,
n_rdm=n_rdm,
n_cv=n_cv)
if n_rdm > 0 or n_pattern > 0:
nc = cv_noise_ceiling(sample,
ceil_set,
test_set,
method=method,
pattern_descriptor=pattern_descriptor)
else:
nc = boot_noise_ceiling(sample,
method=method,
rdm_descriptor=rdm_descriptor)
noise_ceil[:, i_sample] = nc
for idx in range(len(test_set)):
test_set[idx][1] = _concat_sampling(pattern_idx,
test_set[idx][1])
train_set[idx][1] = _concat_sampling(pattern_idx,
train_set[idx][1])
cv_result = crossval(models,
sample,
train_set,
test_set,
method=method,
fitter=fitter,
pattern_descriptor=pattern_descriptor,
calc_noise_ceil=False)
evaluations[i_sample, :, :] = cv_result.evaluations[0]
else: # sample does not allow desired crossvalidation
evaluations[i_sample, :, :] = np.nan
noise_ceil[:, i_sample] = np.nan
if boot_type == 'both':
cv_method = 'bootstrap_crossval'
dof = min(data.n_rdm, data.n_cond) - 1
elif boot_type == 'pattern':
cv_method = 'bootstrap_crossval_pattern'
dof = data.n_cond - 1
elif boot_type == 'rdm':
cv_method = 'bootstrap_crossval_rdm'
dof = data.n_rdm - 1
eval_ok = ~np.isnan(evaluations[:, 0, 0])
if use_correction and n_cv > 1:
# we essentially project from the two points for 1 repetition and
# for n_cv repetitions to infinitely many cv repetitions
evals_mean = np.mean(evaluations[eval_ok], -1)
evals_1 = evaluations[eval_ok]
noise_ceil_mean = np.mean(noise_ceil[:, eval_ok], -1)
noise_ceil_1 = noise_ceil[:, eval_ok]
var_mean = np.cov(np.concatenate([evals_mean.T, noise_ceil_mean]))
var_1 = []
for i in range(n_cv):
var_1.append(
np.cov(
np.concatenate([evals_1[:, :, i].T, noise_ceil_1[:, :,
i]])))
var_1 = np.mean(np.array(var_1), axis=0)
# this is the main formula for the correction:
variances = (n_cv * var_mean - var_1) / (n_cv - 1)
else:
if use_correction:
raise Warning('correction requested, but only one cv run' +
' per sample requested. This is invalid!' +
' We do not use the correction for now.')
evals_nonan = np.mean(np.mean(evaluations[eval_ok], -1), -1)
noise_ceil_nonan = np.mean(noise_ceil[:, eval_ok], -1)
variances = np.cov(np.concatenate([evals_nonan.T, noise_ceil_nonan]))
result = Result(models,
evaluations,
method=method,
cv_method=cv_method,
noise_ceiling=noise_ceil,
variances=variances,
dof=dof)
return result
def eval_bootstrap(models,
data,
theta=None,
method='cosine',
N=1000,
pattern_descriptor='index',
rdm_descriptor='index',
boot_noise_ceil=True):
"""evaluates models on data
performs bootstrapping to get a sampling distribution
Args:
models(rsatoolbox.model.Model or list): models to be evaluated
data(rsatoolbox.rdm.RDMs): data to evaluate on
theta(numpy.ndarray): parameter vector for the models
method(string): comparison method to use
N(int): number of samples
pattern_descriptor(string): descriptor to group patterns for bootstrap
rdm_descriptor(string): descriptor to group rdms for bootstrap
Returns:
numpy.ndarray: vector of evaluations
"""
models, evaluations, theta, _ = \
input_check_model(models, theta, None, N)
noise_min = []
noise_max = []
for i in tqdm.trange(N):
sample, rdm_idx, pattern_idx = \
bootstrap_sample(data, rdm_descriptor=rdm_descriptor,
pattern_descriptor=pattern_descriptor)
if len(np.unique(pattern_idx)) >= 3:
for j, mod in enumerate(models):
rdm_pred = mod.predict_rdm(theta=theta[j])
rdm_pred = rdm_pred.subsample_pattern(pattern_descriptor,
pattern_idx)
evaluations[i, j] = np.mean(compare(rdm_pred, sample, method))
if boot_noise_ceil:
noise_min_sample, noise_max_sample = boot_noise_ceiling(
sample, method=method, rdm_descriptor=rdm_descriptor)
noise_min.append(noise_min_sample)
noise_max.append(noise_max_sample)
else:
evaluations[i, :] = np.nan
noise_min.append(np.nan)
noise_max.append(np.nan)
if boot_noise_ceil:
eval_ok = np.isfinite(evaluations[:, 0])
noise_ceil = np.array([noise_min, noise_max])
variances = np.cov(
np.concatenate([evaluations[eval_ok, :].T, noise_ceil[:,
eval_ok]]))
else:
eval_ok = np.isfinite(evaluations[:, 0])
noise_ceil = np.array(
boot_noise_ceiling(data,
method=method,
rdm_descriptor=rdm_descriptor))
variances = np.cov(evaluations[eval_ok, :].T)
dof = min(data.n_rdm, data.n_cond) - 1
result = Result(models,
evaluations,
method=method,
cv_method='bootstrap',
noise_ceiling=noise_ceil,
variances=variances,
dof=dof)
return result