diff --git a/src/data_handling/data_distributor.py b/src/data_handling/data_distributor.py
index c6015ed78956fca539a951d6b0072ca8924a7a9c..e8c6044280799ded080ab4bff3627aeb9ffde2db 100644
--- a/src/data_handling/data_distributor.py
+++ b/src/data_handling/data_distributor.py
@@ -57,8 +57,8 @@ class Distributor(keras.utils.Sequence):
if self.upsampling:
try:
s = self.generator.get_data_generator(k)
- x_total = np.concatenate([x_total, np.copy(s.extremes_history.copy())], axis=0)
- y_total = np.concatenate([y_total, np.copy(s.extremes_labels.copy())], axis=0)
+ x_total = np.concatenate([x_total, np.copy(s.get_extremes_history())], axis=0)
+ y_total = np.concatenate([y_total, np.copy(s.get_extremes_label())], axis=0)
except AttributeError: # no extremes history / labels available, copy will fail
pass
# get number of mini batches
diff --git a/src/data_handling/data_preparation.py b/src/data_handling/data_preparation.py
index 4751bd35d0852a7044260e5b08a1e203234e1e1b..490d661195aa017113f705da7b2e1e896e55fdc1 100644
--- a/src/data_handling/data_preparation.py
+++ b/src/data_handling/data_preparation.py
@@ -61,7 +61,7 @@ class DataPrep(object):
self.label = None
self.observation = None
self.extremes_history = None
- self.extremes_labels = None
+ self.extremes_label = None
self.kwargs = kwargs
self.data = None
self.meta = None
@@ -424,6 +424,12 @@ class DataPrep(object):
def get_transposed_label(self):
return self.label.squeeze("Stations").transpose("datetime", "window").copy()
+ def get_extremes_history(self):
+ return self.extremes_history.transpose("datetime", "window", "Stations", "variables").copy()
+
+ def get_extremes_label(self):
+ return self.extremes_label.squeeze("Stations").transpose("datetime", "window").copy()
+
def multiply_extremes(self, extreme_values: num_or_list = 1., extremes_on_right_tail_only: bool = False,
timedelta: Tuple[int, str] = (1, 'm')):
"""
@@ -450,7 +456,7 @@ class DataPrep(object):
for extr_val in sorted(extreme_values):
# check if some extreme values are already extracted
- if (self.extremes_labels is None) or (self.extremes_history is None):
+ if (self.extremes_label is None) or (self.extremes_history is None):
# extract extremes based on occurance in labels
if extremes_on_right_tail_only:
extreme_label_idx = (self.label > extr_val).any(axis=0).values.reshape(-1,)
@@ -462,23 +468,21 @@ class DataPrep(object):
extremes_history = self.history[..., extreme_label_idx, :]
extremes_label.datetime.values += np.timedelta64(*timedelta)
extremes_history.datetime.values += np.timedelta64(*timedelta)
- self.extremes_labels = extremes_label.squeeze('Stations').transpose('datetime', 'window')
- self.extremes_history = extremes_history.transpose('datetime', 'window', 'Stations', 'variables')
- else: # one extr value iteration is done already: self.extremes_labels is NOT None...
+ self.extremes_label = extremes_label#.squeeze('Stations').transpose('datetime', 'window')
+ self.extremes_history = extremes_history#.transpose('datetime', 'window', 'Stations', 'variables')
+ else: # one extr value iteration is done already: self.extremes_label is NOT None...
if extremes_on_right_tail_only:
- extreme_label_idx = (self.extremes_labels > extr_val).any(axis=1).values.reshape(-1,)
+ extreme_label_idx = (self.extremes_label > extr_val).any(axis=0).values.reshape(-1, )
else:
- extreme_label_idx = np.concatenate(((self.extremes_labels < -extr_val).any(axis=1
- ).values.reshape(-1, 1),
- (self.extremes_labels > extr_val).any(axis=1
- ).values.reshape(-1, 1)
+ extreme_label_idx = np.concatenate(((self.extremes_label < -extr_val).any(axis=0).values.reshape(-1, 1),
+ (self.extremes_label > extr_val).any(axis=0).values.reshape(-1, 1)
), axis=1).any(axis=1)
# check on existing extracted extremes to minimise computational costs for comparison
- extremes_label = self.extremes_labels[extreme_label_idx, ...]
- extremes_history = self.extremes_history[extreme_label_idx, ...]
+ extremes_label = self.extremes_label[..., extreme_label_idx]
+ extremes_history = self.extremes_history[..., extreme_label_idx, :]
extremes_label.datetime.values += np.timedelta64(*timedelta)
extremes_history.datetime.values += np.timedelta64(*timedelta)
- self.extremes_labels = xr.concat([self.extremes_labels, extremes_label], dim='datetime')
+ self.extremes_label = xr.concat([self.extremes_label, extremes_label], dim='datetime')
self.extremes_history = xr.concat([self.extremes_history, extremes_history], dim='datetime')
diff --git a/test/test_data_handling/test_data_distributor.py b/test/test_data_handling/test_data_distributor.py
index dd0ca99dde0ed38bfbbd392f65eeddca78bbb075..15344fd808a4aa9ee5774ad8ba647bf5ce06d015 100644
--- a/test/test_data_handling/test_data_distributor.py
+++ b/test/test_data_handling/test_data_distributor.py
@@ -98,3 +98,21 @@ class TestDistributor:
assert np.testing.assert_equal(x, x_perm) is None
assert np.testing.assert_equal(y, y_perm) is None
+ def test_distribute_on_batches_upsampling_no_extremes_given(self, generator, model):
+ d = Distributor(generator, model, upsampling=True)
+ gen_len = d.generator.get_data_generator(0, load_local_tmp_storage=False).get_transposed_label().shape[0]
+ num_mini_batches = math.ceil(gen_len / d.batch_size)
+ i = 0
+ for i, e in enumerate(d.distribute_on_batches(fit_call=False)):
+ assert e[0].shape[0] <= d.batch_size
+ assert i + 1 == num_mini_batches
+
+ def test_distribute_on_batches_upsampling(self, generator, model):
+ generator.extreme_values = [1]
+ d = Distributor(generator, model, upsampling=True)
+ gen_len = d.generator.get_data_generator(0, load_local_tmp_storage=False).get_transposed_label().shape[0]
+ extr_len = d.generator.get_data_generator(0, load_local_tmp_storage=False).get_extremes_label().shape[0]
+ i = 0
+ for i, e in enumerate(d.distribute_on_batches(fit_call=False)):
+ assert e[0].shape[0] <= d.batch_size
+ assert i + 1 == math.ceil((gen_len + extr_len) / d.batch_size)
diff --git a/test/test_data_handling/test_data_generator.py b/test/test_data_handling/test_data_generator.py
index 9bf11154609afa9ada2b488455f7a341a41d21ae..939f93cc9ee01c76a282e755aca14b39c6fc4ac9 100644
--- a/test/test_data_handling/test_data_generator.py
+++ b/test/test_data_handling/test_data_generator.py
@@ -238,6 +238,20 @@ class TestDataGenerator:
assert data._transform_method == "standardise"
assert data.mean is not None
+ def test_get_data_generator_extremes(self, gen_with_transformation):
+ gen = gen_with_transformation
+ gen.kwargs = {"statistics_per_var": {'o3': 'dma8eu', 'temp': 'maximum'}}
+ gen.extreme_values = [1.]
+ data = gen.get_data_generator("DEBW107", load_local_tmp_storage=False, save_local_tmp_storage=False)
+ assert data.extremes_label is not None
+ assert data.extremes_history is not None
+ assert data.extremes_label.shape[:2] == data.label.shape[:2]
+ assert data.extremes_label.shape[2] <= data.label.shape[2]
+ len_both_tails = data.extremes_label.shape[2]
+ gen.kwargs["extremes_on_right_tail_only"] = True
+ data = gen.get_data_generator("DEBW107", load_local_tmp_storage=False, save_local_tmp_storage=False)
+ assert data.extremes_label.shape[2] <= len_both_tails
+
def test_save_pickle_data(self, gen):
file = os.path.join(gen.data_path_tmp, f"DEBW107_{'_'.join(sorted(gen.variables))}_2010_2014_.pickle")
if os.path.exists(file):
diff --git a/test/test_data_handling/test_data_preparation.py b/test/test_data_handling/test_data_preparation.py
index f202a6efc09502c635ad548471b588e259ebc7e1..71f3a1d6a0a675a155b517901aef1f3c359b104b 100644
--- a/test/test_data_handling/test_data_preparation.py
+++ b/test/test_data_handling/test_data_preparation.py
@@ -410,7 +410,7 @@ class TestDataPrep:
data.make_labels("variables", "o3", "datetime", 2)
orig = data.label
data.multiply_extremes(1)
- upsampled = data.extremes_labels
+ upsampled = data.extremes_label
assert (upsampled > 1).sum() == (orig > 1).sum()
assert (upsampled < -1).sum() == (orig < -1).sum()
@@ -420,7 +420,7 @@ class TestDataPrep:
data.make_labels("variables", "o3", "datetime", 2)
orig = data.label
data.multiply_extremes([1, 1.5, 2, 3])
- upsampled = data.extremes_labels
+ upsampled = data.extremes_label
def f(d, op, n):
return op(d, n).any(dim="window").sum()
assert f(upsampled, gt, 1) == sum([f(orig, gt, 1), f(orig, gt, 1.5), f(orig, gt, 2) * 2, f(orig, gt, 3) * 4])
@@ -438,9 +438,29 @@ class TestDataPrep:
data.make_labels("variables", "o3", "datetime", 2)
orig = data.label
data.multiply_extremes([1, 2], extremes_on_right_tail_only=True)
- upsampled = data.extremes_labels
+ upsampled = data.extremes_label
def f(d, op, n):
return op(d, n).any(dim="window").sum()
assert f(upsampled, gt, 1) == sum([f(orig, gt, 1), f(orig, gt, 2)])
- assert len(upsampled) == sum([f(orig, gt, 1), f(orig, gt, 2)])
+ assert upsampled.shape[2] == sum([f(orig, gt, 1), f(orig, gt, 2)])
assert f(upsampled, lt, -1) == 0
+
+ def test_get_extremes_history(self, data):
+ data.transform("datetime")
+ data.make_history_window("variables", 3, "datetime")
+ data.make_labels("variables", "o3", "datetime", 2)
+ data.make_observation("variables", "o3", "datetime")
+ data.remove_nan("datetime")
+ data.multiply_extremes([1, 2], extremes_on_right_tail_only=True)
+ assert (data.get_extremes_history() ==
+ data.extremes_history.transpose("datetime", "window", "Stations", "variables")).all()
+
+ def test_get_extremes_label(self, data):
+ data.transform("datetime")
+ data.make_history_window("variables", 3, "datetime")
+ data.make_labels("variables", "o3", "datetime", 2)
+ data.make_observation("variables", "o3", "datetime")
+ data.remove_nan("datetime")
+ data.multiply_extremes([1, 2], extremes_on_right_tail_only=True)
+ assert (data.get_extremes_label() ==
+ data.extremes_label.squeeze("Stations").transpose("datetime", "window")).all()