def clean_both_sets(self): # Clean by directy changing the cnt variables... clean_train_cnt, clean_test_cnt = clean_train_test_cnt( self.cnt, self.test_cnt, self.cleaner, self.test_cleaner) self.cnt = clean_train_cnt self.test_cnt = clean_test_cnt self.rejected_chan_names = [] # necessary since will be used # in preprocess set... self.rejected_trials = 'unknown' self.clean_trials = 'unknown'
def load(self): # Loading both sets, cleaning cnts and finished log.info("Load Training Set...") self.train_set.signal_processor.load_signal_and_markers() log.info("Load Test Set...") self.test_set.signal_processor.load_signal_and_markers() train_cnt = self.train_set.signal_processor.cnt test_cnt = self.test_set.signal_processor.cnt clean_train_cnt, clean_test_cnt = clean_train_test_cnt(train_cnt, test_cnt,self.train_cleaner, self.test_cleaner) self.train_set.signal_processor.cnt = clean_train_cnt self.test_set.signal_processor.cnt = clean_test_cnt assert np.array_equal(self.train_set.signal_processor.cnt.axes[1], self.train_set.signal_processor.cnt.axes[1]), ("Sensor names should " "be the same for train and test...") log.info("Create sets from cleaned cnt...") # in case of cnt signal matrix: if isinstance(self.train_set, CntSignalMatrix): self.train_set.load_from_cnt() self.test_set.load_from_cnt() # in case of raw set: elif isinstance(self.train_set, CleanSignalMatrix): for one_set in [self.train_set, self.test_set]: # this is very fragile... as changes to # the original class logic will affect this :( one_set.load_from_cnt() one_set.create_dense_design_matrix() one_set.remove_signal_epo() if one_set.unsupervised_preprocessor is not None: one_set.apply_unsupervised_preprocessor() one_set.y = np.argmax(one_set.y, axis=1).astype(np.int32) else: raise ValueError("Unknown type of train set {:s}".format( self.train_set.__class__.__name__)) log.info("Loaded clean train data with shape {:s}.".format( self.train_set.get_topological_view().shape)) log.info("Loaded clean test data with shape {:s}.".format( self.test_set.get_topological_view().shape)) self.sets = [self.train_set, self.test_set] self.y = self.sets[-1].y[0:1]