Added set_wLasso_profiles() method for weighted lasso regularization …

…using profiles

commit/core.pyx

@@ -764,6 +764,63 @@ cdef class Evaluation :
 
         return y
 
+    def set_wLasso_profiles(self, prof_weights, lambda_perc_IC): 
+        """
+        Compute array of weights for all the streamlines' profiles and set weighted lasso regularisation.
+
+        Parameters
+        ----------
+        prof_weights - np.array(np.float64) :
+            array of weights for the profiles.
+            NB: this array must have the same size as the number of profiles set before.
+            NB: the weights must be greater or equal to 1.
+
+        lambda_perc_IC - float :
+            percentage of the maximum value of the regularisation parameter for the IC compartment.
+            NB: lambda_perc_IC must be a float greater than 0.
+        """
+
+        # check if all the necessary functions have been called
+        if self.niiDWI is None :
+            logger.error( 'Data not loaded; call "load_data()" first' )
+        if self.DICTIONARY is None :
+            logger.error( 'Dictionary not loaded; call "load_dictionary()" first' )
+        if self.KERNELS is None :
+            logger.error( 'Response functions not generated; call "generate_kernels()" and "load_kernels()" first' )
+        if self.THREADS is None :
+            logger.error( 'Threads not set; call "set_threads()" first' )
+        if self.A is None :
+            logger.error( 'Operator not built; call "build_operator()" first' )
+
+        if self.DICTIONARY['IC']['nF'] <= 0 :
+            logger.error( 'No streamline found in the dictionary; check your data' )
+
+        # regularisation['sizeIC'] = int( self.DICTIONARY['IC']['nF'] * self.KERNELS['wmr'].shape[0] * self.KERNELS['wmc'].shape[0]) 
+        # self.DICTIONARY['IC']['nF'] = number of kept streamlines
+        # self.KERNELS['wmr'] = array with shape (1, ndirs, nS), where nS is the number of samples in the scheme
+        # self.KERNELS['wmc'] = array with shape (num_prof, num_samples)
+
+        # check prof_weights
+        if type(prof_weights) not in [list, np.ndarray]:
+            logger.error('"prof_weights" must be a list or a numpy array')
+        prof_weights = np.array(prof_weights, dtype=np.float64)
+        if np.any(prof_weights < 1):
+            logger.error('All group weights must be greater or equal to 1')
+        if prof_weights.size != self.KERNELS['wmc'].shape[0]:
+            logger.error( f'The number of profiles\' weights in the provided array does not match the number of profiles set in "load_kernels()" (got {prof_weights.size} but {self.KERNELS["wmc"].shape[0]} expected)' )
+
+        # compute complete array of weights
+        prof_weights_all = np.repeat(prof_weights, len(self.DICTIONARY["TRK"]["kept"]))
+        dict_ic = {}
+        dict_ic['coeff_weights'] = prof_weights_all
+
+        # set reg
+        self.set_regularisation(
+            regularisers   = ('lasso', None, None), 
+            lambdas        = (lambda_perc_IC, None, None), 
+            is_nonnegative = (False, True, True),
+            params         = (dict_ic, None, None))
+
 
     def set_regularisation(self, regularisers=(None, None, None), lambdas=(None, None, None), is_nonnegative=(True, True, True), params=(None, None, None)):
         """
@@ -960,9 +1017,10 @@ cdef class Evaluation :
             if dictIC_params is not None and 'coeff_weights' in dictIC_params:
                 if np.any(dictIC_params['coeff_weights'] < 0):
                     logger.error('All coefficients weights must be non-negative')
-                if dictIC_params['coeff_weights'].size != len(self.DICTIONARY['TRK']['kept']):
+                dict_kept = np.tile(self.DICTIONARY['TRK']['kept'], self.KERNELS['wmc'].shape[0])
+                if dictIC_params['coeff_weights'].size != dict_kept.size:
                     logger.error(f'"coeff_weights" must have the same size as the number of elements in the IC compartment (got {dictIC_params["coeff_weights"].size} but {len(self.DICTIONARY["TRK"]["kept"])} expected)')
-                dictIC_params['coeff_weights_kept'] = dictIC_params['coeff_weights'][self.DICTIONARY['TRK']['kept']==1]
+                dictIC_params['coeff_weights_kept'] = dictIC_params['coeff_weights'][dict_kept==1]
 
         # check if group parameters are consistent with the regularisation
         if regularisation['regIC'] not in ['group_lasso', 'sparse_group_lasso'] and dictIC_params is not None: