def test_first_fold():
data = np.arange(10)
dataset = DenseDesignMatrixWrapper(topo_view=to_4d_array(data), y=np.zeros(10))
splitter = SingleFoldSplitter(n_folds=10,
i_test_fold=0)
datasets= splitter.split_into_train_valid_test(dataset)
assert np.array_equal(to_4d_array(np.arange(1,9)),
datasets['train'].get_topological_view() )
assert np.array_equal(to_4d_array([9]),
datasets['valid'].get_topological_view() )
assert np.array_equal(to_4d_array([0]),
datasets['test'].get_topological_view() )
def test_first_fold():
data = np.arange(10)
dataset = DenseDesignMatrixWrapper(topo_view=to_4d_array(data),
y=np.zeros(10))
splitter = SingleFoldSplitter(n_folds=10, i_test_fold=0)
datasets = splitter.split_into_train_valid_test(dataset)
assert np.array_equal(to_4d_array(np.arange(1, 9)),
datasets['train'].get_topological_view())
assert np.array_equal(to_4d_array([9]),
datasets['valid'].get_topological_view())
assert np.array_equal(to_4d_array([0]),
datasets['test'].get_topological_view())
def test_repeated_calls_with_shuffle():
"""Repeated calls should always lead to same split"""
data = np.arange(100)
dataset = DenseDesignMatrixWrapper(topo_view=to_4d_array(data),
y=np.zeros(100))
splitter = SingleFoldSplitter(n_folds=10,
i_test_fold=9, shuffle=True)
reference_datasets = splitter.split_into_train_valid_test(dataset)
# 20 attemptsat splitting should all lead to same datasets!
for _ in range(20):
new_datasets = splitter.split_into_train_valid_test(dataset)
for key in reference_datasets:
assert np.array_equal(reference_datasets[key].get_topological_view(),
new_datasets[key].get_topological_view())
def test_repeated_calls_with_shuffle():
"""Repeated calls should always lead to same split"""
data = np.arange(100)
dataset = DenseDesignMatrixWrapper(topo_view=to_4d_array(data),
y=np.zeros(100))
splitter = SingleFoldSplitter(n_folds=10, i_test_fold=9, shuffle=True)
reference_datasets = splitter.split_into_train_valid_test(dataset)
# 20 attemptsat splitting should all lead to same datasets!
for _ in range(20):
new_datasets = splitter.split_into_train_valid_test(dataset)
for key in reference_datasets:
assert np.array_equal(
reference_datasets[key].get_topological_view(),
new_datasets[key].get_topological_view())
def test_preprocessed_splitter():
class DemeanPreproc():
"""Just for tests :)"""
def apply(self, dataset, can_fit=False):
topo_view = dataset.get_topological_view()
if can_fit:
self.mean = np.mean(topo_view)
dataset.set_topological_view(topo_view - self.mean)
data = np.arange(10)
dataset = DenseDesignMatrixWrapper(topo_view=to_4d_array(data),
y=np.zeros(10))
splitter = SingleFoldSplitter(n_folds=10, i_test_fold=9)
preproc_splitter = PreprocessedSplitter(dataset_splitter=splitter,
preprocessor=DemeanPreproc())
first_round_sets = preproc_splitter.get_train_valid_test(dataset)
train_topo = first_round_sets['train'].get_topological_view()
valid_topo = first_round_sets['valid'].get_topological_view()
test_topo = first_round_sets['test'].get_topological_view()
assert np.array_equal(
train_topo, to_4d_array([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]))
assert np.array_equal(valid_topo, to_4d_array([4.5]))
assert np.array_equal(test_topo, to_4d_array([5.5]))
second_round_set = preproc_splitter.get_train_merged_valid_test(dataset)
train_topo = second_round_set['train'].get_topological_view()
valid_topo = second_round_set['valid'].get_topological_view()
test_topo = second_round_set['test'].get_topological_view()
assert np.array_equal(train_topo,
to_4d_array([-4, -3, -2, -1, 0, 1, 2, 3, 4]))
assert np.array_equal(valid_topo, to_4d_array([4]))
assert np.array_equal(test_topo, to_4d_array([5]))
def run(self):
self.all_layers = []
self.all_monitor_chans = []
for i_fold in range(self.n_folds):
log.info("Running fold {:d} of {:d}".format(
i_fold + 1, self.n_folds))
this_layers = deepcopy(self.final_layer)
this_exp_args = deepcopy(self.exp_args)
## make sure dataset is loaded...
self.dataset.ensure_is_loaded()
dataset_splitter = SingleFoldSplitter(n_folds=self.n_folds,
i_test_fold=i_fold,
shuffle=self.shuffle)
exp = Experiment(this_layers, self.dataset, dataset_splitter,
**this_exp_args)
exp.setup()
exp.run()
self.all_layers.append(deepcopy(exp.final_layer))
self.all_monitor_chans.append(deepcopy(exp.monitor_chans))
