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[REF] Rename kernel_estimator attribute to kernel_transformer #197

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May 14, 2020
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[REF] Rename kernel_estimator attribute to kernel_transformer #197

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36 changes: 18 additions & 18 deletions nimare/meta/cbma/ale.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,11 +37,11 @@ class ALE(CBMAEstimator):

Parameters
----------
kernel_estimator : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
kernel_transformer : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
Kernel with which to convolve coordinates from dataset. Default is
ALEKernel.
**kwargs
Keyword arguments. Arguments for the kernel_estimator can be assigned
Keyword arguments. Arguments for the kernel_transformer can be assigned
here, with the prefix '\kernel__' in the variable name.

Notes
Expand All @@ -62,29 +62,29 @@ class ALE(CBMAEstimator):
.. [3] Eickhoff, Simon B., et al. "Activation likelihood estimation
meta-analysis revisited." Neuroimage 59.3 (2012): 2349-2361.
"""
def __init__(self, kernel_estimator=ALEKernel, **kwargs):
def __init__(self, kernel_transformer=ALEKernel, **kwargs):
kernel_args = {k.split('kernel__')[1]: v for k, v in kwargs.items()
if k.startswith('kernel__')}
kwargs = {k: v for k, v in kwargs.items() if not k.startswith('kernel__')}
for k in kwargs.keys():
LGR.warning('Keyword argument "{0}" not recognized'.format(k))

if not issubclass(kernel_estimator, KernelTransformer):
raise ValueError('Argument "kernel_estimator" must be a '
if not issubclass(kernel_transformer, KernelTransformer):
raise ValueError('Argument "kernel_transformer" must be a '
'KernelTransformer')

self.mask = None
self.dataset = None

self.kernel_estimator = kernel_estimator(**kernel_args)
self.kernel_transformer = kernel_transformer(**kernel_args)
self.results = None
self.null_distributions = {}

def _fit(self, dataset):
self.dataset = dataset
self.mask = dataset.masker.mask_img

ma_maps = self.kernel_estimator.transform(self.dataset, mask=self.mask, masked=False)
ma_maps = self.kernel_transformer.transform(self.dataset, mask=self.mask, masked=False)
ale_values = self._compute_ale(ma_maps)
self._compute_null(ma_maps)
p_values, z_values = self._ale_to_p(ale_values)
Expand All @@ -102,7 +102,7 @@ def _compute_ale(self, data):
Returns masked array of ALE values.
"""
if isinstance(data, pd.DataFrame):
ma_values = self.kernel_estimator.transform(data, mask=self.mask, masked=True)
ma_values = self.kernel_transformer.transform(data, mask=self.mask, masked=True)
elif isinstance(data, list):
ma_values = apply_mask(data, self.mask)
elif isinstance(data, np.ndarray):
Expand Down Expand Up @@ -438,10 +438,10 @@ def _fit(self, ale1, ale2, image1=None, image2=None, ma_maps1=None,
grp2_voxel = image2 > 0

if ma_maps1 is None:
ma_maps1 = ale1.kernel_estimator.transform(ale1.dataset, masked=False)
ma_maps1 = ale1.kernel_transformer.transform(ale1.dataset, masked=False)

if ma_maps2 is None:
ma_maps2 = ale2.kernel_estimator.transform(ale2.dataset, masked=False)
ma_maps2 = ale2.kernel_transformer.transform(ale2.dataset, masked=False)

n_grp1 = len(ma_maps1)
ma_maps = ma_maps1 + ma_maps2
Expand Down Expand Up @@ -569,11 +569,11 @@ class SCALE(CBMAEstimator):
Tab-delimited file of coordinates from database or numpy array with ijk
coordinates. Voxels are rows and i, j, k (meaning matrix-space) values
are the three columnns.
kernel_estimator : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
kernel_transformer : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
Kernel with which to convolve coordinates from dataset. Default is
ALEKernel.
**kwargs
Keyword arguments. Arguments for the kernel_estimator can be assigned
Keyword arguments. Arguments for the kernel_transformer can be assigned
here, with the prefix '\kernel__' in the variable name.

References
Expand All @@ -583,12 +583,12 @@ class SCALE(CBMAEstimator):
(2014): 559-570. https://doi.org/10.1016/j.neuroimage.2014.06.007
"""
def __init__(self, voxel_thresh=0.001, n_iters=10000, n_cores=-1, ijk=None,
kernel_estimator=ALEKernel, **kwargs):
kernel_transformer=ALEKernel, **kwargs):
kernel_args = {k.split('kernel__')[1]: v for k, v in kwargs.items()
if k.startswith('kernel__')}

if not issubclass(kernel_estimator, KernelTransformer):
raise ValueError('Argument "kernel_estimator" must be a '
if not issubclass(kernel_transformer, KernelTransformer):
raise ValueError('Argument "kernel_transformer" must be a '
'KernelTransformer')

kwargs = {k: v for k, v in kwargs.items() if not k.startswith('kernel__')}
Expand All @@ -598,7 +598,7 @@ def __init__(self, voxel_thresh=0.001, n_iters=10000, n_cores=-1, ijk=None,
self.mask = None
self.dataset = None

self.kernel_estimator = kernel_estimator(**kernel_args)
self.kernel_transformer = kernel_transformer(**kernel_args)
self.voxel_thresh = voxel_thresh
self.ijk = ijk
self.n_iters = n_iters
Expand Down Expand Up @@ -628,7 +628,7 @@ def _fit(self, dataset):
self.dataset = dataset
self.mask = dataset.masker.mask_img

ma_maps = self.kernel_estimator.transform(self.dataset, masked=False)
ma_maps = self.kernel_transformer.transform(self.dataset, masked=False)

max_poss_ale = 1.
for ma_map in ma_maps:
Expand Down Expand Up @@ -683,7 +683,7 @@ def _compute_ale(self, df=None, ma_maps=None):
Returns masked array of ALE values and 1XnBins null distribution.
"""
if df is not None:
ma_maps = self.kernel_estimator.transform(df, self.mask, masked=True)
ma_maps = self.kernel_transformer.transform(df, self.mask, masked=True)
ma_values = ma_maps
else:
assert ma_maps is not None
Expand Down
52 changes: 26 additions & 26 deletions nimare/meta/cbma/mkda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,11 +29,11 @@ class MKDADensity(CBMAEstimator):

Parameters
----------
kernel_estimator : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
kernel_transformer : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
Kernel with which to convolve coordinates from dataset. Default is
MKDAKernel.
**kwargs
Keyword arguments. Arguments for the kernel_estimator can be assigned
Keyword arguments. Arguments for the kernel_transformer can be assigned
here, with the prefix '\kernel__' in the variable name.

Notes
Expand All @@ -47,19 +47,19 @@ class MKDADensity(CBMAEstimator):
cognitive and affective neuroscience 2.2 (2007): 150-158.
https://doi.org/10.1093/scan/nsm015
"""
def __init__(self, kernel_estimator=MKDAKernel, **kwargs):
def __init__(self, kernel_transformer=MKDAKernel, **kwargs):
kernel_args = {k.split('kernel__')[1]: v for k, v in kwargs.items()
if k.startswith('kernel__')}

if not issubclass(kernel_estimator, KernelTransformer):
raise ValueError('Argument "kernel_estimator" must be a '
if not issubclass(kernel_transformer, KernelTransformer):
raise ValueError('Argument "kernel_transformer" must be a '
'KernelTransformer')

kwargs = {k: v for k, v in kwargs.items() if not k.startswith('kernel__')}
for k in kwargs.keys():
LGR.warning('Keyword argument "{0}" not recognized'.format(k))

self.kernel_estimator = kernel_estimator(**kernel_args)
self.kernel_transformer = kernel_transformer(**kernel_args)

self.mask = None
self.dataset = None
Expand All @@ -77,7 +77,7 @@ def _fit(self, dataset):
self.dataset = dataset
self.mask = dataset.masker.mask_img

ma_values = self.kernel_estimator.transform(dataset, masked=True)
ma_values = self.kernel_transformer.transform(dataset, masked=True)

# Weight each SCM by square root of sample size
ids_df = self.dataset.coordinates.groupby('id').first()
Expand All @@ -104,7 +104,7 @@ def _run_fwe_permutation(self, params):
iter_ijk, iter_df, conn, voxel_thresh = params
iter_ijk = np.squeeze(iter_ijk)
iter_df[['i', 'j', 'k']] = iter_ijk
iter_ma_maps = self.kernel_estimator.transform(iter_df, mask=self.mask, masked=True)
iter_ma_maps = self.kernel_transformer.transform(iter_df, mask=self.mask, masked=True)
iter_ma_maps *= self.weight_vec
iter_of_map = np.sum(iter_ma_maps, axis=0)
iter_max_value = np.max(iter_of_map)
Expand Down Expand Up @@ -236,11 +236,11 @@ class MKDAChi2(CBMAEstimator):
prior : float, optional
Uniform prior probability of each feature being active in a map in
the absence of evidence from the map. Default: 0.5
kernel_estimator : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
kernel_transformer : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
Kernel with which to convolve coordinates from dataset. Default is
MKDAKernel.
**kwargs
Keyword arguments. Arguments for the kernel_estimator can be assigned
Keyword arguments. Arguments for the kernel_transformer can be assigned
here, with the prefix '\kernel__' in the variable name.

Notes
Expand All @@ -255,19 +255,19 @@ class MKDAChi2(CBMAEstimator):
cognitive and affective neuroscience 2.2 (2007): 150-158.
https://doi.org/10.1093/scan/nsm015
"""
def __init__(self, prior=0.5, kernel_estimator=MKDAKernel, **kwargs):
def __init__(self, prior=0.5, kernel_transformer=MKDAKernel, **kwargs):
kernel_args = {k.split('kernel__')[1]: v for k, v in kwargs.items()
if k.startswith('kernel__')}

if not issubclass(kernel_estimator, KernelTransformer):
raise ValueError('Argument "kernel_estimator" must be a '
if not issubclass(kernel_transformer, KernelTransformer):
raise ValueError('Argument "kernel_transformer" must be a '
'KernelTransformer')

kwargs = {k: v for k, v in kwargs.items() if not k.startswith('kernel__')}
for k in kwargs.keys():
LGR.warning('Keyword argument "{0}" not recognized'.format(k))

self.kernel_estimator = kernel_estimator(**kernel_args)
self.kernel_transformer = kernel_transformer(**kernel_args)
self.prior = prior

def fit(self, dataset, dataset2):
Expand Down Expand Up @@ -295,8 +295,8 @@ def _fit(self, dataset, dataset2):
self.dataset2 = dataset2
self.mask = dataset.masker.mask_img

ma_maps1 = self.kernel_estimator.transform(self.dataset, mask=self.mask, masked=True)
ma_maps2 = self.kernel_estimator.transform(self.dataset2, mask=self.mask, masked=True)
ma_maps1 = self.kernel_transformer.transform(self.dataset, mask=self.mask, masked=True)
ma_maps2 = self.kernel_transformer.transform(self.dataset2, mask=self.mask, masked=True)

# Calculate different count variables
n_selected = ma_maps1.shape[0]
Expand Down Expand Up @@ -365,8 +365,8 @@ def _run_fwe_permutation(self, params):
iter_df1[['i', 'j', 'k']] = iter_ijk1
iter_df2[['i', 'j', 'k']] = iter_ijk2

temp_ma_maps1 = self.kernel_estimator.transform(iter_df1, self.mask, masked=True)
temp_ma_maps2 = self.kernel_estimator.transform(iter_df2, self.mask, masked=True)
temp_ma_maps1 = self.kernel_transformer.transform(iter_df1, self.mask, masked=True)
temp_ma_maps2 = self.kernel_transformer.transform(iter_df2, self.mask, masked=True)

n_selected = temp_ma_maps1.shape[0]
n_unselected = temp_ma_maps2.shape[0]
Expand Down Expand Up @@ -565,11 +565,11 @@ class KDA(CBMAEstimator):

Parameters
----------
kernel_estimator : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
kernel_transformer : :obj:`nimare.meta.cbma.base.KernelTransformer`, optional
Kernel with which to convolve coordinates from dataset. Default is
KDAKernel.
**kwargs
Keyword arguments. Arguments for the kernel_estimator can be assigned
Keyword arguments. Arguments for the kernel_transformer can be assigned
here, with the prefix '\kernel__' in the variable name.

Notes
Expand All @@ -588,19 +588,19 @@ class KDA(CBMAEstimator):
studies of shifting attention: a meta-analysis." Neuroimage 22.4
(2004): 1679-1693. https://doi.org/10.1016/j.neuroimage.2004.03.052
"""
def __init__(self, kernel_estimator=KDAKernel, **kwargs):
def __init__(self, kernel_transformer=KDAKernel, **kwargs):
kernel_args = {k.split('kernel__')[1]: v for k, v in kwargs.items()
if k.startswith('kernel__')}

if not issubclass(kernel_estimator, KernelTransformer):
raise ValueError('Argument "kernel_estimator" must be a '
if not issubclass(kernel_transformer, KernelTransformer):
raise ValueError('Argument "kernel_transformer" must be a '
'KernelTransformer')

kwargs = {k: v for k, v in kwargs.items() if not k.startswith('kernel__')}
for k in kwargs.keys():
LGR.warning('Keyword argument "{0}" not recognized'.format(k))

self.kernel_estimator = kernel_estimator(**kernel_args)
self.kernel_transformer = kernel_transformer(**kernel_args)

def _fit(self, dataset):
"""
Expand All @@ -614,7 +614,7 @@ def _fit(self, dataset):
self.dataset = dataset
self.mask = dataset.masker.mask_img

ma_maps = self.kernel_estimator.transform(dataset, masked=True)
ma_maps = self.kernel_transformer.transform(dataset, masked=True)
of_values = np.sum(ma_maps, axis=0)
images = {
'of': of_values
Expand All @@ -625,7 +625,7 @@ def _run_fwe_permutation(self, params):
iter_ijk, iter_df = params
iter_ijk = np.squeeze(iter_ijk)
iter_df[['i', 'j', 'k']] = iter_ijk
iter_ma_maps = self.kernel_estimator.transform(iter_df, mask=self.mask, masked=True)
iter_ma_maps = self.kernel_transformer.transform(iter_df, mask=self.mask, masked=True)
iter_of_map = np.sum(iter_ma_maps, axis=0)
iter_max_value = np.max(iter_of_map)
return iter_max_value
Expand Down
4 changes: 2 additions & 2 deletions nimare/parcellate/mamp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ def __init__(self, dataset, ids):
self.solutions = None
self.metrics = None

def fit(self, target_mask, n_parcels=2, kernel_estimator=ALEKernel,
def fit(self, target_mask, n_parcels=2, kernel_transformer=ALEKernel,
**kwargs):
"""
Run MAMP parcellation.
Expand Down Expand Up @@ -87,7 +87,7 @@ def fit(self, target_mask, n_parcels=2, kernel_estimator=ALEKernel,
if not isinstance(n_parcels, list):
n_parcels = [n_parcels]

k_est = kernel_estimator(self.coordinates, self.mask)
k_est = kernel_transformer(self.coordinates, self.mask)
ma_maps = k_est.transform(self.ids, **kernel_args)

# Step 1: Build correlation matrix
Expand Down