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| Original file line number | Diff line number | Diff line change |
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| @@ -1,163 +0,0 @@ | ||
| import numpy as np | ||
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| from pyitab.utils.dataset import get_ds_data | ||
| from pyitab.analysis.utils import get_rois, get_params | ||
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| from pyitab.preprocessing import FeatureSlicer | ||
| from pyitab.analysis.base import Analyzer | ||
| from pyitab.preprocessing.base import Transformer | ||
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| from scipy.io.matlab.mio import savemat | ||
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| from joblib import Parallel, delayed | ||
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| import logging | ||
| logger = logging.getLogger(__name__) | ||
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| # TODO: TEST | ||
| class RoiAnalyzer(Analyzer): | ||
| """ | ||
| """ | ||
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| def __init__(self, analysis=Analyzer(), n_jobs=-1, **kwargs): | ||
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| self.analysis = analysis | ||
| self.n_jobs = n_jobs | ||
| Analyzer.__init__(self, | ||
| **kwargs, | ||
| ) | ||
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| def fit(self, ds, roi='all', | ||
| roi_values=None, | ||
| prepro=Transformer(), | ||
| **kwargs): | ||
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| """[summary] | ||
| Parameters | ||
| ---------- | ||
| ds : [type] | ||
| [description] | ||
| cv_attr : str, optional | ||
| [description] (the default is 'chunks', which [default_description]) | ||
| roi : list, optional | ||
| list of strings that must be present in ds.fa keys | ||
| (the default is 'all', which [default_description]) | ||
| roi_values : list, optional | ||
| A list of key, value tuple where the key is the | ||
| roi name, specified in ds.fa.roi and value is the value of the | ||
| subroi. (the default is None, which [default_description]) | ||
| prepro : [type], optional | ||
| [description] (the default is Transformer(), which [default_description]) | ||
| return_predictions : bool, optional | ||
| [description] (the default is False, which [default_description]) | ||
| return_splits : bool, optional | ||
| [description] (the default is True, which [default_description]) | ||
| Returns | ||
| ------- | ||
| [type] | ||
| [description] | ||
| """ | ||
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| if roi_values is None: | ||
| roi_values = get_rois(ds, roi) | ||
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| scores = dict() | ||
| # TODO: How to use multiple ROIs | ||
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| scores = Parallel(n_jobs=self.n_jobs) \ | ||
| (delayed(self._parallel_roi)(ds, r, v, prepro, **kwargs) | ||
| for r, v in roi_values) | ||
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| """ | ||
| for r, value in roi_values: | ||
| ds_ = FeatureSlicer(**{r: value}).transform(ds) | ||
| ds_ = prepro.transform(ds_) | ||
| logger.info("Dataset shape %s" % (str(ds_.shape))) | ||
| self.analysis.fit(ds_, **kwargs) | ||
| string_value = "+".join([str(v) for v in value]) | ||
| scores["mask-%s_value-%s" % (r, string_value)] = self.analysis.scores.copy() | ||
| """ | ||
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| self.scores = scores | ||
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| self._info = self._store_info(ds) | ||
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| return self | ||
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| def _parallel_roi(self, ds, roi, value, prepro, **kwargs): | ||
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| ds_ = FeatureSlicer(**{roi: value}).transform(ds) | ||
| ds_ = prepro.transform(ds_) | ||
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| logger.info("Dataset shape %s" % (str(ds_.shape))) | ||
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| self.analysis.fit(ds_, **kwargs) | ||
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| string_value = "+".join([str(v) for v in value]) | ||
| key = "mask-%s_value-%s" % (roi, string_value) | ||
| value = self.analysis.scores.copy() | ||
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| return (key, value) | ||
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| def save(self, path=None, **kwargs): | ||
| """[summary] | ||
| Parameters | ||
| ---------- | ||
| path : [type], optional | ||
| [description] (the default is None, which [default_description]) | ||
| Returns | ||
| ------- | ||
| [type] | ||
| [description] | ||
| <source_keywords>_task-<task>_mask-<mask>_ | ||
| value-<roi_value>_distance-<datetime>_<key>-<value>_data.mat | ||
| """ | ||
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| import os | ||
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| path, prefix = Analyzer.save(self, path=path, **kwargs) | ||
| kwargs.update({'prefix': prefix}) | ||
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| mat_score = dict() | ||
| for roi, scores in self.scores.items(): | ||
| kwargs.update({'mask': roi}) | ||
| filename = self._get_filename(**kwargs) | ||
| logger.info("Saving %s" % (filename)) | ||
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| mat_score['test_score'] = scores | ||
| savemat(os.path.join(path, filename), mat_score) | ||
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| return | ||
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| def _get_filename(self, **kwargs): | ||
| "target-<values>_id-<datetime>_mask-<mask>_value-<roi_value>_data.mat" | ||
| logger.debug(kwargs) | ||
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| params = {'analysis': self.analysis.name} | ||
| params_ = self._get_prepro_info(**kwargs) | ||
| params.update(params_) | ||
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| logger.debug(params) | ||
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| # TODO: Solve empty prefix | ||
| prefix = kwargs.pop('prefix') | ||
| midpart = "_".join(["%s-%s" % (k, str(v).replace("_", "+")) \ | ||
| for k, v in params.items()]) | ||
| trailing = kwargs.pop('mask') | ||
| filename = "%s_data.mat" % ("_".join([prefix, midpart, trailing])) | ||
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| return filename | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,203 @@ | ||
| import numpy as np | ||
|
|
||
| from pyitab.utils.dataset import get_ds_data | ||
| from pyitab.analysis.utils import get_rois, get_params | ||
|
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| from pyitab.preprocessing import FeatureSlicer | ||
| from pyitab.analysis.base import Analyzer | ||
| from pyitab.preprocessing.base import Transformer | ||
| from pyitab.preprocessing.permutation import Permutator | ||
|
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| from scipy.io.matlab.mio import savemat | ||
|
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| from joblib import Parallel, delayed | ||
|
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| import logging | ||
| logger = logging.getLogger(__name__) | ||
|
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|
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| # TODO: TEST | ||
| class RoiWrapper(Analyzer): | ||
| """RoiWrapper is a wrapper class that can be used to iterate the analysis on | ||
| a subset of features, usually is performed on ROI. | ||
| Parameters | ||
| ---------- | ||
| analysis : [type], optional | ||
| [description], by default Analyzer() | ||
| n_jobs : int, optional | ||
| [description], by default -1 | ||
| """ | ||
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| def __init__(self, analysis=Analyzer(), n_jobs=-1, **kwargs): | ||
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| self.analysis = analysis | ||
| self.n_jobs = n_jobs | ||
| Analyzer.__init__(self, | ||
| **kwargs, | ||
| ) | ||
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|
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| def fit(self, ds, roi='all', | ||
| roi_values=None, | ||
| prepro=Transformer(), | ||
| **kwargs): | ||
|
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| """Fit the analysis on a set of features, specified in the `roi` | ||
| attribute. | ||
| Parameters | ||
| ---------- | ||
| ds : [type] | ||
| [description] | ||
| roi : list, optional | ||
| list of strings that must be present in ds.fa keys | ||
| (the default is 'all', which [default_description]) | ||
| roi_values : list, optional | ||
| A list of key, value tuple where the key is the | ||
| roi name, specified in ds.fa.roi and value is the value of the | ||
| subroi. (the default is None, which [default_description]) | ||
| prepro : [type], optional | ||
| [description] (the default is Transformer(), which [default_description]) | ||
| return_predictions : bool, optional | ||
| [description] (the default is False, which [default_description]) | ||
| return_splits : bool, optional | ||
| [description] (the default is True, which [default_description]) | ||
| Returns | ||
| ------- | ||
| [type] | ||
| [description] | ||
| """ | ||
|
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| if roi_values is None: | ||
| roi_values = get_rois(ds, roi) | ||
|
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| scores = dict() | ||
| # TODO: How to use multiple ROIs | ||
|
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| scores = [self._parallel(ds, r, v, prepro, **kwargs) for r, v in roi_values] | ||
|
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| """ | ||
| scores = Parallel(n_jobs=self.n_jobs, verbose=1) \ | ||
| (delayed(self._parallel)(ds, r, v, prepro, **kwargs) | ||
| for r, v in roi_values) | ||
| """ | ||
| self.scores = scores | ||
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| self._info = self._store_info(ds) | ||
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| return self | ||
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| def _parallel(self, ds, roi, value, prepro, **kwargs): | ||
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| ds_ = FeatureSlicer(**{roi: value}).transform(ds) | ||
| ds_ = prepro.transform(ds_) | ||
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| logger.info("Dataset shape %s" % (str(ds_.shape))) | ||
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| self.analysis.fit(ds_, **kwargs) | ||
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| string_value = "+".join([str(v) for v in value]) | ||
| key = "mask-%s_value-%s" % (roi, string_value) | ||
| value = self.analysis.scores.copy() | ||
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| return (key, value) | ||
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|
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| def save(self, path=None, **kwargs): | ||
| """[summary] | ||
| Parameters | ||
| ---------- | ||
| path : [type], optional | ||
| [description] (the default is None, which [default_description]) | ||
| Returns | ||
| ------- | ||
| [type] | ||
| [description] | ||
| <source_keywords>_task-<task>_mask-<mask>_ | ||
| value-<roi_value>_distance-<datetime>_<key>-<value>_data.mat | ||
| """ | ||
|
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| import os | ||
|
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| path, prefix = Analyzer.save(self, path=path, **kwargs) | ||
| kwargs.update({'prefix': prefix}) | ||
|
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| mat_score = dict() | ||
| for roi, scores in self.scores.items(): | ||
| kwargs.update({'mask': roi}) | ||
| filename = self._get_filename(**kwargs) | ||
| logger.info("Saving %s" % (filename)) | ||
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| self.analysis.save(path) | ||
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| #mat_score['test_score'] = scores | ||
| #savemat(os.path.join(path, filename), mat_score) | ||
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| return | ||
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| def _get_filename(self, **kwargs): | ||
| "target-<values>_id-<datetime>_mask-<mask>_value-<roi_value>_data.mat" | ||
| logger.debug(kwargs) | ||
|
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| params = {'analysis': self.analysis.name} | ||
| params_ = self._get_prepro_info(**kwargs) | ||
| params.update(params_) | ||
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| logger.debug(params) | ||
|
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| # TODO: Solve empty prefix | ||
| prefix = kwargs.pop('prefix') | ||
| midpart = "_".join(["%s-%s" % (k, str(v).replace("_", "+")) \ | ||
| for k, v in params.items()]) | ||
| trailing = kwargs.pop('mask') | ||
| filename = "%s_data.mat" % ("_".join([prefix, midpart, trailing])) | ||
|
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| return filename | ||
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| class PermutationWrapper(Analyzer): | ||
| def __init__(self, analysis=Analyzer(), n_jobs=-1, n=100, **kwargs): | ||
|
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| self.analysis = analysis | ||
| self.n_jobs = n_jobs | ||
| self.n_permutations = n + 1 | ||
| self._permutator = Permutator(n=n) | ||
| Analyzer.__init__(self, | ||
| **kwargs, | ||
| ) | ||
|
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| return | ||
|
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| def fit(self, ds, **kwargs): | ||
|
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| scores = dict() | ||
| # TODO: How to use multiple ROIs | ||
|
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| scores = Parallel(n_jobs=self.n_jobs, verbose=1) \ | ||
| (delayed(self._parallel_roi)(ds, **kwargs) | ||
| for _ in np.arange(self.n_permutations)) | ||
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| self.scores = scores | ||
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| self._info = self._store_info(ds) | ||
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| return self | ||
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| def _parallel(self, ds, **kwargs): | ||
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| ds_ = self._permutator.transform(ds) | ||
| logger.info("Dataset shape %s" % (str(ds_.shape))) | ||
|
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| self.analysis.fit(ds_, **kwargs) | ||
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| return self.analysis.scores.copy() |
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