Beispiel #1
0
    def __init__(self,
                 caps_directory,
                 subjects_visits_tsv,
                 diagnoses_tsv,
                 group_id,
                 image_type,
                 output_dir,
                 fwhm=0,
                 modulated="on",
                 pvc=None,
                 precomputed_kernel=None,
                 mask_zeros=True,
                 n_threads=15,
                 n_iterations=100,
                 test_size=0.3,
                 n_learning_points=10,
                 grid_search_folds=10,
                 balanced=True,
                 c_range=np.logspace(-6, 2, 17)):
        self._output_dir = output_dir
        self._n_threads = n_threads
        self._n_iterations = n_iterations
        self._test_size = test_size
        self._n_learning_points = n_learning_points
        self._grid_search_folds = grid_search_folds
        self._balanced = balanced
        self._c_range = c_range

        self._input = input.CAPSVoxelBasedInput(
            caps_directory, subjects_visits_tsv, diagnoses_tsv, group_id,
            image_type, fwhm, modulated, pvc, mask_zeros, precomputed_kernel)

        self._validation = None
        self._algorithm = None
Beispiel #2
0
    def __init__(self,
                 caps_directory,
                 subjects_visits_tsv,
                 diagnoses_tsv,
                 group_id,
                 image_type,
                 output_dir,
                 fwhm=0,
                 modulated="on",
                 pvc=None,
                 precomputed_kernel=None,
                 mask_zeros=True,
                 n_threads=15,
                 n_iterations=100,
                 test_size=0.3,
                 grid_search_folds=10,
                 balanced=True,
                 c_range=np.logspace(-6, 2, 17),
                 splits_indices=None,
                 feature_selection_method=None,
                 feature_rescaling_method='zscore',
                 top_k=50):
        self._output_dir = output_dir
        self._n_threads = n_threads
        self._n_iterations = n_iterations
        self._test_size = test_size
        self._grid_search_folds = grid_search_folds
        self._balanced = balanced
        self._c_range = c_range
        self._splits_indices = splits_indices
        self._feature_rescaling_method = feature_rescaling_method
        self._feature_selection_method = feature_selection_method
        self._top_k = top_k

        self._input = input.CAPSVoxelBasedInput(
            caps_directory, subjects_visits_tsv, diagnoses_tsv, group_id,
            image_type, fwhm, modulated, pvc, mask_zeros, precomputed_kernel)

        self._validation = None
        self._algorithm = None
Beispiel #3
0
    def __init__(self,
                 caps_directory,
                 subjects_visits_tsv,
                 diagnoses_tsv,
                 group_id,
                 image_type,
                 output_dir,
                 fwhm=0,
                 modulated="on",
                 pvc=None,
                 precomputed_kernel=None,
                 mask_zeros=True,
                 n_threads=15,
                 n_folds=10,
                 grid_search_folds=10,
                 balanced=True,
                 c_range=np.logspace(-6, 2, 17)):

        # Here some parameters selected for this task

        self._output_dir = output_dir
        self._n_threads = n_threads
        self._n_folds = n_folds
        self._grid_search_folds = grid_search_folds
        self._balanced = balanced
        self._c_range = c_range

        # In this case we are running a voxel based input approach
        #

        self._input = input.CAPSVoxelBasedInput(
            caps_directory, subjects_visits_tsv, diagnoses_tsv, group_id,
            image_type, fwhm, modulated, pvc, mask_zeros, precomputed_kernel)

        # Validation and algorithm will be selected in the next part of code

        self._validation = None
        self._algorithm = None