def run(no_folds = 5, supervised = True, i = 0, l = 2, n = 128, expParameters = {}): inst = KFold(n_splits = no_folds, shuffle=True, random_state=125) exp = experiment.GGCNNExperiment('2018-08-28-SA1SA2', '2018-08-28-SA1SA2', SA1Experiment(neurons = n, blocks = l, **expParameters)) exp.num_iterations = 5000 exp.optimizer = 'adam' exp.loss_type = 'linear' exp.debug = True # Was True exp.preprocess_data(dataset) valid_idx = np.flatnonzero(dataset['level_0']['labels'] >= 0) # Missing data labelled with -1 if supervised: train_idx, test_idx = list(inst.split( valid_idx ))[i] else: test_idx, train_idx = list(inst.split( valid_idx ))[i] # Reversed to get more samples in the test set than the training set n_components = expParameters.get('linkage_adjustment_components', None) exp.create_data(train_idx, test_idx, n_components = n_components) exp.build_network() results = exp.run() # Node type of input nodes: 0 = training set; 1 = test set; -1 = neither idx_split = np.empty((len(dataset['level_0']['labels']), 1)) idx_split.fill(-1) idx_split[train_idx] = 0 idx_split[test_idx] = 1 return results, idx_split
def run(no_folds = 5, supervised = True, i = 0, l = 2, n = 128, expParameters = {}): inst = KFold(n_splits = no_folds, shuffle=True, random_state=125) exp = experiment.GGCNNExperiment('2018-08-28-SA1SA2', '2018-08-28-SA1SA2', SA1Experiment(neurons = n, blocks = l, **expParameters)) exp.num_iterations = 5000 exp.optimizer = 'adam' exp.loss_type = 'linear' exp.debug = True # Was True exp.preprocess_data(dataset) if supervised: train_idx, test_idx = list(inst.split(np.arange(len(dataset[0]))))[i] else: test_idx, train_idx = list(inst.split(np.arange(len(dataset[0]))))[i] # Reversed to get more samples in the test set than the training set exp.create_data(train_idx, test_idx) exp.build_network() results = exp.run() return results
i = int(sys.argv[3]) except IndexError: l = 2 n = 128 i = 0 saveName = 'Output/SemiSupervisedSydney-NoEdge-l={:d}-n={:d}-i={:d}.csv'.format( l, n, i) max_acc = [] iteration = [] layers = [] neurons = [] rep = [] exp = experiment.GGCNNExperiment('2018-06-06-SA1', '2018-06-06-sa1', SA1Experiment(neurons=n, blocks=l)) # exp = experiment.SingleGraphCNNExperiment('2018-06-06-SA1', '2018-06-06-sa1', SA1Experiment(neurons = n, blocks = l)) exp.num_iterations = 1000 exp.optimizer = 'adam' exp.debug = True # Was True exp.preprocess_data(dataset) train_idx, test_idx = list(inst.split(np.arange(len(dataset[-1]))))[i] # test_idx, train_idx = list(inst.split(np.arange(len(dataset[-1]))))[i] # Reversed to get more samples in the test set than the training set exp.create_data(train_idx, test_idx) exp.build_network() results = exp.run()
net.make_embedding_layer(self.neurons) net.make_graphcnn_layer(1, name='final', with_bn=False, with_act_func=False) no_folds = 5 ## inst = KFold(n_splits=no_folds, shuffle=True, random_state=125) l = 2 n = 64 i = 2 exp = experiment.GGCNNExperiment('2018-08-28-SA1SA2', '2018-08-28-SA1SA2', SA1Experiment(neurons=n, blocks=l)) exp.num_iterations = 2000 exp.optimizer = 'adam' exp.loss_type = "linear" exp.debug = True # Was True exp.preprocess_data(dataset) train_idx, test_idx = list(inst.split(np.arange(len(dataset[0]))))[i] # print('Before: ', exp.train_idx.shape) # exp.train_idx = np.append(exp.train_idx, np.arange( SA1DatasetSize , len(dataset[-1] ))) # exp.test_idx = np.append(exp.test_idx, np.arange( SA1DatasetSize , len(dataset[-1] ))) # print('After: ', exp.train_idx.shape) # test_idx, train_idx = list(inst.split(np.arange(len(dataset[-1]))))[i] # Reversed to get more samples in the test set than the training set