def _SelectKHighest_from_mask(self, X, y, roi_path):
print('Selecting K Highest from mask: %s' % roi_path)
'''
roi_mask = masking.apply_mask(
roi_path, self.mask_non_brain, k_features=self.k_features
)
'''
roi_mask = masking.apply_mask(roi_path, self.mask_non_brain)
print('SelectKHighest ROI mask size: %d' % roi_mask.astype(bool).sum())
from pymri.utils.masking import separate_k_highest
roi_mask = separate_k_highest(self.k_features, roi_mask)
roi_mask = roi_mask.astype(bool)
import nibabel
mask_brain_img = nibabel.load(self.mask_non_brain).get_data()
mask_brain = mask_brain_img.flatten().astype(bool)
roi = np.zeros(mask_brain.flatten().shape)
roi[mask_brain] = roi_mask
roi = roi.reshape(mask_brain_img.shape)
img = nibabel.Nifti1Image(roi, np.eye(4))
img.to_filename('/tmp/best_roi.nii.gz')
print('SelectKHighest data reduction from: %s' % str(X.shape))
X = X[..., roi_mask]
print('SelectKHighest data reduction to: %s' % str(X.shape))
self.feature_reduction_method = roi_path
return X
def _roi_mask_apply(self, X, roi_path):
print('Applying mask: %s' % roi_path)
roi_mask = masking.apply_mask(roi_path, self.mask_non_brain)
roi_mask = roi_mask.astype(bool)
print('ROI mask apply ROI mask size: %s' % str(roi_mask.sum()))
X = X[..., roi_mask]
self.feature_reduction_method = roi_path
print('Masked data has shape: %s' % str(X.shape))
return X
def _roi_mask_apply(self, X, roi_path):
print('Applying mask: %s' % roi_path)
roi_mask = masking.apply_mask(roi_path, self.mask_non_brain)
roi_mask = roi_mask.astype(bool)
print('ROI mask apply ROI mask size: %s' % str(roi_mask.sum()))
X = X[..., roi_mask]
self.feature_reduction_method = roi_path
print('Masked data has shape: %s' % str(X.shape))
return X
def _SelectKHighest_from_mask(self, X, y, roi_path):
print('Selecting K Highest from mask: %s' % roi_path)
'''
roi_mask = masking.apply_mask(
roi_path, self.mask_non_brain, k_features=self.k_features
)
'''
roi_mask = masking.apply_mask(roi_path, self.mask_non_brain)
print('SelectKHighest ROI mask size: %d' % roi_mask.astype(bool).sum())
from pymri.utils.masking import separate_k_highest
roi_mask = separate_k_highest(self.k_features, roi_mask)
roi_mask = roi_mask.astype(bool)
import nibabel
mask_brain_img = nibabel.load(self.mask_non_brain).get_data()
mask_brain = mask_brain_img.flatten().astype(bool)
roi = np.zeros(mask_brain.flatten().shape)
roi[mask_brain] = roi_mask
roi = roi.reshape(mask_brain_img.shape)
img = nibabel.Nifti1Image(roi, np.eye(4))
img.to_filename('/tmp/best_roi.nii.gz')
print('SelectKHighest data reduction from: %s' % str(X.shape))
X = X[..., roi_mask]
print('SelectKHighest data reduction to: %s' % str(X.shape))
self.feature_reduction_method = roi_path
return X
nibabel.load('/git/pymri/examples/MNI152_T1_2mm_brain.nii.gz').get_data()
# X.shape is (91,109, 91, 216)
# and mask.shape is (91, 109, 91)
affine = np.array([[-2., 0., 0., 90.], [0., 2., 0., -126.], [0., 0., 2., -72.],
[0., 0., 0., 1.]])
usage_print()
# ### Masking step
from pymri.utils import masking, signal
from nibabel import Nifti1Image
# Mask data
X0_img = Nifti1Image(X0, affine)
X0 = masking.apply_mask(X0_img, mask, smoothing_fwhm=4)
X1_img = Nifti1Image(X1, affine)
X1 = masking.apply_mask(X1_img, mask, smoothing_fwhm=4)
# # Standardize data
# from sklearn import preprocessing
# for sample in range(len(X0)):
# X0[sample] = preprocessing.scale(X0[sample])
# for sample in range(len(X1)):
# X1[sample] = preprocessing.scale(X1[sample])
X = np.zeros(shape=(
(len(X0) + len(X1)),
X0.shape[-1],
))
y = np.zeros(shape=((len(y0) + len(y1)), ))
def load_data(self):
# create sklearn's Bunch of data
dataset_files = Bunch(
func=self.bold,
session_target=self.attr,
mask=self.mask_brain,
conditions_target=self.attr_lit
)
# fmri_data and mask are copied to break reference to
# the original object
bold_img = nibabel.load(dataset_files.func)
fmri_data = bold_img.get_data().astype(float)
affine = bold_img.get_affine()
y, session = np.loadtxt(dataset_files.session_target).astype("int").T
conditions = np.recfromtxt(dataset_files.conditions_target)['f0']
mask = dataset_files.mask
self.mask_non_brain = mask
# ### Restrict to specified conditions
condition_mask = np.zeros(shape=(conditions.shape[0]))
# unifrom contrasts, e.g.: contrast=(('face', 'table'), 'house')):
# face:0, table:1, house:2
# [0, 0, 2, 1, 2, 0, 1, 1] ==> [0, 0, 2, 0, 2, 0, 0, 0]
for n in self.contrast:
if type(n) == tuple:
# first label
k_uniform = n[0]
k_uniform = y[conditions == k_uniform][0]
for k in n:
condition_mask += conditions == k
# unifying subclasses into one class
# (string label doesn't matter)
y[conditions == k] = k_uniform
else:
condition_mask += conditions == n
condition_mask = np.array(condition_mask, dtype=bool)
X = fmri_data[..., condition_mask]
y = y[condition_mask]
# adjust all (target) labels to range(0:n_classes range),
# e.g. [4,4,2,8,9] ==> [1,1,0,2,3]
# e.g. [0, 0, 2, 0, 2, 0, 0, 0] ==> [0, 0, 1, 0, 1, 0, 0, 0]
cnt = 0
for val in np.unique(y):
if val > cnt:
y[y == val] = cnt
cnt += 1
# ### Masking step
# Mask data using brain mask (remove non-brain regions)
X_img = Nifti1Image(X, affine)
X = masking.apply_mask(X_img, mask, smoothing_fwhm=4)
# X = signal.clean(X, standardize=True, detrend=False)
print('##############################')
print('# dataset loaded successfully ')
print('# X shape: %s' % str(X.shape))
print('# y shape: %s' % str(y.shape))
print('##############################')
self.condition_mask = condition_mask
self.X_raw, self.y = X, y
# condition_mask = np.logical_or.reduce((conditions == 'face', conditions == 'house', conditions == 'cat')) # X = fmri_data[..., condition_mask] # y = y[condition_mask] X = fmri_data y = y # session = session[condition_mask] # conditions = conditions[condition_mask] # ### Masking step # from utils import masking, signal from pymri.utils import masking from nibabel import Nifti1Image # Mask data X_img = Nifti1Image(X, affine) X = masking.apply_mask(X_img, mask, smoothing_fwhm=4) # X = signal.clean(X, standardize=True, detrend=False) # ### Sampling ################################################################ from sklearn.cross_validation import train_test_split # split original dataset into training and testing datasets X, X_t, y, y_t = train_test_split(X, y, test_size=0.4, random_state=42) ############################################################################### # # F-score # ############################################################################### from sklearn.feature_selection import f_classif f_values, p_values = f_classif(X, y)
# ### Restrict to faces and houses
condition_mask = np.logical_or(conditions == 'face', conditions == 'house')
X = fmri_data[..., condition_mask]
y = y[condition_mask]
# session = session[condition_mask]
# conditions = conditions[condition_mask]
# ### Masking step
# from utils import masking, signal
from pymri.utils import masking
from nibabel import Nifti1Image
# Mask data
X_img = Nifti1Image(X, affine)
X = masking.apply_mask(X_img, mask, smoothing_fwhm=4)
# X = signal.clean(X, standardize=True, detrend=False)
# ### Sampling ################################################################
from sklearn.cross_validation import train_test_split
# split original dataset into training and testing datasets
X, X_t, y, y_t = train_test_split(
X, y, test_size=0.4, random_state=42
)
###############################################################################
#
# F-score
#
###############################################################################
def load_nifti(data_dir,
Y,
k_features=784,
normalize=True,
scale_0_1=False,
vectorize_target=False,
reshape=False):
'''
Parameters
----------
data_dir : string.
Location of the data files (bold.nii.gz, attributes.txt,
attributes_literal.txt).
Y : ndtuple of strings.
Classes. Label space will be reduced to specified conditions.
Returns
-------
X : ndnumpy array.
Samples containing features.
y : ndnumpy array.
Labels, targets, classes.
conditions:
Y = {Y_1, Y_2, ... , Y_n}
Y_n = S = {S_1, S_2, .... , S_k}
where:
Y - set of classes
S - set of subclasses
n - number of classes
k - number of subclasses
classes consists of subclasses
'''
import numpy as np
import nibabel
from sklearn.datasets.base import Bunch
# create sklearn's Bunch of data
dataset_files = Bunch(func=data_dir + 'bold.nii.gz',
session_target=data_dir + 'attributes.txt',
mask=data_dir + 'mask.nii.gz',
conditions_target=data_dir +
'attributes_literal.txt')
# fmri_data and mask are copied to break reference to the original object
bold_img = nibabel.load(dataset_files.func)
fmri_data = bold_img.get_data().astype(float)
affine = bold_img.get_affine()
y, session = np.loadtxt(dataset_files.session_target).astype("int").T
conditions = np.recfromtxt(dataset_files.conditions_target)['f0']
mask = dataset_files.mask
# ### Restrict to specified conditions
condition_mask = np.zeros(shape=(conditions.shape[0]))
for n in Y:
if type(n) == tuple:
# first label
k_uniform = n[0]
k_uniform = y[conditions == k_uniform][0]
for k in n:
condition_mask += conditions == k
# unifying subclasses into one class
# (string label doesn't matter)
y[conditions == k] = k_uniform
else:
condition_mask += conditions == n
condition_mask = np.array(condition_mask, dtype=bool)
X = fmri_data[..., condition_mask]
y = y[condition_mask]
# adjust all labels to 0:n_classes range, e.g. [4,4,2,8,9] ==> [1,1,0,2,3]
cnt = 0
for val in np.unique(y):
if val > cnt:
y[y == val] = cnt
cnt += 1
# ### Masking step
from pymri.utils import masking
from nibabel import Nifti1Image
# Mask data
X_img = Nifti1Image(X, affine)
X = masking.apply_mask(X_img, mask, smoothing_fwhm=4)
from sklearn.feature_selection import SelectKBest, f_classif
# ### Define the dimension reduction to be used.
# Here we use a classical univariate feature selection based on F-test,
# namely Anova. We set the number of features to be selected to 784
feature_selection = SelectKBest(f_classif, k=k_features)
feature_selection.fit(X, y)
X = feature_selection.transform(X)
# normalize data
if normalize:
from sklearn import preprocessing
X = preprocessing.normalize(X)
if scale_0_1:
# scale data in range (0,1)
X = (X - X.min()) / (X.max() - X.min())
if vectorize_target:
# how many classes do we have?
n_classes = len(Y)
# [0, 1, 1] ==> [[1, 0], [0, 1], [0, 1]]
y = vectorize(y, n_classes)
if reshape:
# reshape is needed for nnadl acceptable format
X = np.reshape(X, (X.shape[0], X.shape[1], 1))
y = np.reshape(y, (y.shape[0], y.shape[1], 1))
return X, y
def load_data(self):
# create sklearn's Bunch of data
dataset_files = Bunch(func=self.bold,
session_target=self.attr,
mask=self.mask_brain,
conditions_target=self.attr_lit)
# fmri_data and mask are copied to break reference to
# the original object
bold_img = nibabel.load(dataset_files.func)
fmri_data = bold_img.get_data().astype(float)
affine = bold_img.get_affine()
y, session = np.loadtxt(dataset_files.session_target).astype("int").T
conditions = np.recfromtxt(dataset_files.conditions_target)['f0']
mask = dataset_files.mask
self.mask_non_brain = mask
# ### Restrict to specified conditions
condition_mask = np.zeros(shape=(conditions.shape[0]))
# unifrom contrasts, e.g.: contrast=(('face', 'table'), 'house')):
# face:0, table:1, house:2
# [0, 0, 2, 1, 2, 0, 1, 1] ==> [0, 0, 2, 0, 2, 0, 0, 0]
for n in self.contrast:
if type(n) == tuple:
# first label
k_uniform = n[0]
k_uniform = y[conditions == k_uniform][0]
for k in n:
condition_mask += conditions == k
# unifying subclasses into one class
# (string label doesn't matter)
y[conditions == k] = k_uniform
else:
condition_mask += conditions == n
condition_mask = np.array(condition_mask, dtype=bool)
X = fmri_data[..., condition_mask]
y = y[condition_mask]
# adjust all (target) labels to range(0:n_classes range),
# e.g. [4,4,2,8,9] ==> [1,1,0,2,3]
# e.g. [0, 0, 2, 0, 2, 0, 0, 0] ==> [0, 0, 1, 0, 1, 0, 0, 0]
cnt = 0
for val in np.unique(y):
if val > cnt:
y[y == val] = cnt
cnt += 1
# ### Masking step
# Mask data using brain mask (remove non-brain regions)
X_img = Nifti1Image(X, affine)
X = masking.apply_mask(X_img, mask, smoothing_fwhm=4)
# X = signal.clean(X, standardize=True, detrend=False)
print('##############################')
print('# dataset loaded successfully ')
print('# X shape: %s' % str(X.shape))
print('# y shape: %s' % str(y.shape))
print('##############################')
self.condition_mask = condition_mask
self.X_raw, self.y = X, y
def load_theano_dataset(data_dir):
"""
00. Load data from file (X and y).
01. Split into training phase (train dataset) and validation phase.
02. Split validation phase into valdation dataset and test dataset.
Datasets proportions:
(train/validation/test) (0.5/0.25/0.25)
"""
import numpy as np
import nibabel
from sklearn.datasets.base import Bunch
data_dir = data_dir
# create sklearn's Bunch of data
dataset_files = Bunch(
func=data_dir+'bold.nii.gz',
session_target=data_dir+'attributes.txt',
mask=data_dir+'mask.nii.gz',
conditions_target=data_dir+'attributes_literal.txt'
)
# fmri_data and mask are copied to break reference to the original object
bold_img = nibabel.load(dataset_files.func)
fmri_data = bold_img.get_data().astype(float)
affine = bold_img.get_affine()
y, session = np.loadtxt(dataset_files.session_target).astype("int").T
conditions = np.recfromtxt(dataset_files.conditions_target)['f0']
mask = dataset_files.mask
# ### Restrict to specified conditions
condition_mask = np.logical_or(
np.logical_or(
conditions == 'ExeCtrl_5', conditions == 'ExeCtrl_0'
), conditions == 'Rest'
)
X = fmri_data[..., condition_mask]
y = y[condition_mask]
from sklearn.preprocessing import binarize
y = binarize(y, threshold=2.0)[0]
# ### Masking step
from pymri.utils import masking
from nibabel import Nifti1Image
# Mask data
X_img = Nifti1Image(X, affine)
X = masking.apply_mask(X_img, mask, smoothing_fwhm=4)
from sklearn.feature_selection import SelectKBest, f_classif
# ### Define the dimension reduction to be used.
# Here we use a classical univariate feature selection based on F-test,
# namely Anova. We set the number of features to be selected to 784
feature_selection = SelectKBest(f_classif, k=784)
feature_selection.fit(X, y)
X = feature_selection.transform(X)
print(X.shape)
# ### Splitting ###########################################################
from sklearn.cross_validation import train_test_split
# split original dataset into training phase (dataset) and validation phase
X, X_v, y, y_v = train_test_split(
X, y, test_size=0.5, random_state=42
)
# split validation phase into validation dataset and test dataset
X_v, X_t, y_v, y_t = train_test_split(
X_v, y_v, test_size=0.25, random_state=42
)
# X, y - training dataset
# X_v, y_v - validation dataset
# X_t, y_t - test dataset
return (X, y), (X_v, y_v), (X_t, y_t)
def load_nifti(
data_dir, Y, k_features=784,
normalize=True, scale_0_1=False,
vectorize_target=False, reshape=False
):
'''
Parameters
----------
data_dir : string.
Location of the data files (bold.nii.gz, attributes.txt,
attributes_literal.txt).
Y : ndtuple of strings.
Classes. Label space will be reduced to specified conditions.
Returns
-------
X : ndnumpy array.
Samples containing features.
y : ndnumpy array.
Labels, targets, classes.
conditions:
Y = {Y_1, Y_2, ... , Y_n}
Y_n = S = {S_1, S_2, .... , S_k}
where:
Y - set of classes
S - set of subclasses
n - number of classes
k - number of subclasses
classes consists of subclasses
'''
import numpy as np
import nibabel
from sklearn.datasets.base import Bunch
# create sklearn's Bunch of data
dataset_files = Bunch(
func=data_dir+'bold.nii.gz',
session_target=data_dir+'attributes.txt',
mask=data_dir+'mask.nii.gz',
conditions_target=data_dir+'attributes_literal.txt'
)
# fmri_data and mask are copied to break reference to the original object
bold_img = nibabel.load(dataset_files.func)
fmri_data = bold_img.get_data().astype(float)
affine = bold_img.get_affine()
y, session = np.loadtxt(dataset_files.session_target).astype("int").T
conditions = np.recfromtxt(dataset_files.conditions_target)['f0']
mask = dataset_files.mask
# ### Restrict to specified conditions
condition_mask = np.zeros(shape=(conditions.shape[0]))
for n in Y:
if type(n) == tuple:
# first label
k_uniform = n[0]
k_uniform = y[conditions == k_uniform][0]
for k in n:
condition_mask += conditions == k
# unifying subclasses into one class
# (string label doesn't matter)
y[conditions == k] = k_uniform
else:
condition_mask += conditions == n
condition_mask = np.array(condition_mask, dtype=bool)
X = fmri_data[..., condition_mask]
y = y[condition_mask]
# adjust all labels to 0:n_classes range, e.g. [4,4,2,8,9] ==> [1,1,0,2,3]
cnt = 0
for val in np.unique(y):
if val > cnt:
y[y == val] = cnt
cnt += 1
# ### Masking step
from pymri.utils import masking
from nibabel import Nifti1Image
# Mask data
X_img = Nifti1Image(X, affine)
X = masking.apply_mask(X_img, mask, smoothing_fwhm=4)
from sklearn.feature_selection import SelectKBest, f_classif
# ### Define the dimension reduction to be used.
# Here we use a classical univariate feature selection based on F-test,
# namely Anova. We set the number of features to be selected to 784
feature_selection = SelectKBest(f_classif, k=k_features)
feature_selection.fit(X, y)
X = feature_selection.transform(X)
# normalize data
if normalize:
from sklearn import preprocessing
X = preprocessing.normalize(X)
if scale_0_1:
# scale data in range (0,1)
X = (X - X.min()) / (X.max() - X.min())
if vectorize_target:
# how many classes do we have?
n_classes = len(Y)
# [0, 1, 1] ==> [[1, 0], [0, 1], [0, 1]]
y = vectorize(y, n_classes)
if reshape:
# reshape is needed for nnadl acceptable format
X = np.reshape(X, (X.shape[0], X.shape[1], 1))
y = np.reshape(y, (y.shape[0], y.shape[1], 1))
return X, y
def load_theano_dataset(data_dir):
"""
00. Load data from file (X and y).
01. Split into training phase (train dataset) and validation phase.
02. Split validation phase into valdation dataset and test dataset.
Datasets proportions:
(train/validation/test) (0.5/0.25/0.25)
"""
import numpy as np
import nibabel
from sklearn.datasets.base import Bunch
data_dir = data_dir
# create sklearn's Bunch of data
dataset_files = Bunch(func=data_dir + 'bold.nii.gz',
session_target=data_dir + 'attributes.txt',
mask=data_dir + 'mask.nii.gz',
conditions_target=data_dir +
'attributes_literal.txt')
# fmri_data and mask are copied to break reference to the original object
bold_img = nibabel.load(dataset_files.func)
fmri_data = bold_img.get_data().astype(float)
affine = bold_img.get_affine()
y, session = np.loadtxt(dataset_files.session_target).astype("int").T
conditions = np.recfromtxt(dataset_files.conditions_target)['f0']
mask = dataset_files.mask
# ### Restrict to specified conditions
condition_mask = np.logical_or(
np.logical_or(conditions == 'ExeCtrl_5', conditions == 'ExeCtrl_0'),
conditions == 'Rest')
X = fmri_data[..., condition_mask]
y = y[condition_mask]
from sklearn.preprocessing import binarize
y = binarize(y, threshold=2.0)[0]
# ### Masking step
from pymri.utils import masking
from nibabel import Nifti1Image
# Mask data
X_img = Nifti1Image(X, affine)
X = masking.apply_mask(X_img, mask, smoothing_fwhm=4)
from sklearn.feature_selection import SelectKBest, f_classif
# ### Define the dimension reduction to be used.
# Here we use a classical univariate feature selection based on F-test,
# namely Anova. We set the number of features to be selected to 784
feature_selection = SelectKBest(f_classif, k=784)
feature_selection.fit(X, y)
X = feature_selection.transform(X)
print(X.shape)
# ### Splitting ###########################################################
from sklearn.cross_validation import train_test_split
# split original dataset into training phase (dataset) and validation phase
X, X_v, y, y_v = train_test_split(X, y, test_size=0.5, random_state=42)
# split validation phase into validation dataset and test dataset
X_v, X_t, y_v, y_t = train_test_split(X_v,
y_v,
test_size=0.25,
random_state=42)
# X, y - training dataset
# X_v, y_v - validation dataset
# X_t, y_t - test dataset
return (X, y), (X_v, y_v), (X_t, y_t)
affine = np.array([
[ -2., 0., 0., 90.],
[ 0., 2., 0., -126.],
[ 0., 0., 2., -72.],
[ 0., 0., 0., 1.]
])
usage_print()
# ### Masking step
from pymri.utils import masking, signal
from nibabel import Nifti1Image
# Mask data
X0_img = Nifti1Image(X0, affine)
X0 = masking.apply_mask(X0_img, mask, smoothing_fwhm=4)
X1_img = Nifti1Image(X1, affine)
X1 = masking.apply_mask(X1_img, mask, smoothing_fwhm=4)
# # Standardize data
# from sklearn import preprocessing
# for sample in range(len(X0)):
# X0[sample] = preprocessing.scale(X0[sample])
# for sample in range(len(X1)):
# X1[sample] = preprocessing.scale(X1[sample])
X = np.zeros(shape=((len(X0) + len(X1)), X0.shape[-1],))
y = np.zeros(shape=((len(y0) + len(y1)),))
usage_print()