def test_predefined_split(self, cv_split_cls, data):
from sklearn.model_selection import PredefinedSplit
indices = (data.y > 0).astype(int)
split = PredefinedSplit(indices)
dataset_train, dataset_valid = cv_split_cls(split)(data)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert (y_train > 0).all()
assert (y_valid == 0).all()
def test_with_list_of_arrays(self, cv_split_cls, data):
data.X = [data.X, data.X]
m = self.num_samples // 5
n = self.num_samples - m
dataset_train, dataset_valid = cv_split_cls(5)(data)
X_train, y_train = data_from_dataset(dataset_train)
X_valid, y_valid = data_from_dataset(dataset_valid)
assert len(X_train[0]) == len(X_train[1]) == len(y_train) == n
assert len(X_valid[0]) == len(X_valid[1]) == len(y_valid) == m
def test_predefined_split(self, cv_split_cls, data):
from sklearn.model_selection import PredefinedSplit
indices = (data.y > 0).astype(int)
split = PredefinedSplit(indices)
dataset_train, dataset_valid = cv_split_cls(split)(data)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert (y_train > 0).all()
assert (y_valid == 0).all()
def test_with_list_of_arrays(self, cv_split_cls, data):
data.X = [data.X, data.X]
m = self.num_samples // 5
n = self.num_samples - m
dataset_train, dataset_valid = cv_split_cls(5)(data)
X_train, y_train = data_from_dataset(dataset_train)
X_valid, y_valid = data_from_dataset(dataset_valid)
assert len(X_train[0]) == len(X_train[1]) == len(y_train) == n
assert len(X_valid[0]) == len(X_valid[1]) == len(y_valid) == m
def test_with_dict(self, cv_split_cls, data):
data.X = {'1': data.X, '2': data.X}
dataset_train, dataset_valid = cv_split_cls(5)(data)
m = self.num_samples // 5
n = self.num_samples - m
X_train, y_train = data_from_dataset(dataset_train)
X_valid, y_valid = data_from_dataset(dataset_valid)
assert len(X_train['1']) == len(X_train['2']) == len(y_train) == n
assert len(X_valid['1']) == len(X_valid['2']) == len(y_valid) == m
def test_with_dict(self, cv_split_cls, data):
data.X = {'1': data.X, '2': data.X}
dataset_train, dataset_valid = cv_split_cls(5)(data)
m = self.num_samples // 5
n = self.num_samples - m
X_train, y_train = data_from_dataset(dataset_train)
X_valid, y_valid = data_from_dataset(dataset_valid)
assert len(X_train['1']) == len(X_train['2']) == len(y_train) == n
assert len(X_valid['1']) == len(X_valid['2']) == len(y_valid) == m
def test_not_stratified(self, cv_split_cls, data, cv):
num_expected = self.num_samples // 4
y = np.hstack([np.repeat([0, 0, 0], num_expected),
np.repeat([1], num_expected)])
data.y = y
dataset_train, dataset_valid = cv_split_cls(
cv, stratified=False)(data, y)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
# when not stratified, we cannot know the distribution of targets
assert y_train.sum() + y_valid.sum() == num_expected
def test_stratified(self, cv_split_cls, data, cv):
num_expected = self.num_samples // 4
y = np.hstack([np.repeat([0, 0, 0], num_expected),
np.repeat([1], num_expected)])
data.y = y
dataset_train, dataset_valid = cv_split_cls(
cv, stratified=True)(data, y)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert y_train.sum() == 0.8 * num_expected
assert y_valid.sum() == 0.2 * num_expected
def test_y_str_val_stratified(self, cv_split_cls, data):
y = np.array(['a', 'a', 'a', 'b'] * (self.num_samples // 4))
if len(data.X) != len(y):
raise ValueError
data.y = y
dataset_train, dataset_valid = cv_split_cls(5, stratified=True)(data,
y)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert np.isclose(np.mean(y_train == 'b'), 0.25)
assert np.isclose(np.mean(y_valid == 'b'), 0.25)
def test_with_torch_tensors_and_stratified(self, cv_split_cls, data):
num_expected = self.num_samples // 4
data.X = to_tensor(data.X, device='cpu')
y = np.hstack([np.repeat([0, 0, 0], num_expected),
np.repeat([1], num_expected)])
data.y = to_tensor(y, device='cpu')
dataset_train, dataset_valid = cv_split_cls(5, stratified=True)(data, y)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert y_train.sum() == 0.8 * num_expected
assert y_valid.sum() == 0.2 * num_expected
def test_not_stratified(self, cv_split_cls, data, cv):
num_expected = self.num_samples // 4
y = np.hstack([np.repeat([0, 0, 0], num_expected),
np.repeat([1], num_expected)])
data.y = y
dataset_train, dataset_valid = cv_split_cls(
cv, stratified=False)(data, y)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
# when not stratified, we cannot know the distribution of targets
assert y_train.sum() + y_valid.sum() == num_expected
def test_y_str_val_stratified(self, cv_split_cls, data):
y = np.array(['a', 'a', 'a', 'b'] * (self.num_samples // 4))
if len(data.X) != len(y):
raise ValueError
data.y = y
dataset_train, dataset_valid = cv_split_cls(
5, stratified=True)(data, y)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert np.isclose(np.mean(y_train == 'b'), 0.25)
assert np.isclose(np.mean(y_valid == 'b'), 0.25)
def test_with_torch_tensors_and_stratified(self, cv_split_cls, data):
num_expected = self.num_samples // 4
data.X = to_tensor(data.X, device='cpu')
y = np.hstack([np.repeat([0, 0, 0], num_expected),
np.repeat([1], num_expected)])
data.y = to_tensor(y, device='cpu')
dataset_train, dataset_valid = cv_split_cls(5, stratified=True)(data, y)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert y_train.sum() == 0.8 * num_expected
assert y_valid.sum() == 0.2 * num_expected
def test_stratified(self, cv_split_cls, data, cv):
num_expected = self.num_samples // 4
y = np.hstack([np.repeat([0, 0, 0], num_expected),
np.repeat([1], num_expected)])
data.y = y
dataset_train, dataset_valid = cv_split_cls(
cv, stratified=True)(data, y)
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert y_train.sum() == 0.8 * num_expected
assert y_valid.sum() == 0.2 * num_expected
def test_with_y_none(self, cv_split_cls, data):
data.y = None
m = self.num_samples // 5
n = self.num_samples - m
dataset_train, dataset_valid = cv_split_cls(5)(data)
assert len(dataset_train) == n
assert len(dataset_valid) == m
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert y_train is None
assert y_valid is None
def test_with_y_none(self, cv_split_cls, data):
data.y = None
m = self.num_samples // 5
n = self.num_samples - m
dataset_train, dataset_valid = cv_split_cls(5)(data)
assert len(dataset_train) == n
assert len(dataset_valid) == m
y_train = data_from_dataset(dataset_train)[1]
y_valid = data_from_dataset(dataset_valid)[1]
assert y_train is None
assert y_valid is None
def test_with_pandas(self, cv_split_cls, data):
import pandas as pd
data.X = pd.DataFrame(
data.X,
columns=[str(i) for i in range(data.X.shape[1])],
)
dataset_train, dataset_valid = cv_split_cls(5)(data)
m = self.num_samples // 5
X_train, y_train = data_from_dataset(dataset_train)
X_valid, y_valid = data_from_dataset(dataset_valid)
assert len(X_train) + len(X_valid) == self.num_samples
assert len(y_train) + len(y_valid) == self.num_samples
assert len(X_valid) == len(y_valid) == m
def test_with_pandas(self, cv_split_cls, data):
import pandas as pd
data.X = pd.DataFrame(
data.X,
columns=[str(i) for i in range(data.X.shape[1])],
)
dataset_train, dataset_valid = cv_split_cls(5)(data)
m = self.num_samples // 5
X_train, y_train = data_from_dataset(dataset_train)
X_valid, y_valid = data_from_dataset(dataset_valid)
assert len(X_train) + len(X_valid) == self.num_samples
assert len(y_train) + len(y_valid) == self.num_samples
assert len(X_valid) == len(y_valid) == m
def on_epoch_end(self, net, dataset_train, dataset_valid, **kwargs):
EpochScoring.on_epoch_end(self, net, dataset_train, dataset_valid)
X_test, y_test = data_from_dataset(dataset_valid)
y_pred = net.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
history = net.history
history.record("confusion_matrix", cm)
def test_group_kfold(self, cv_split_cls, data):
from sklearn.model_selection import GroupKFold
X, y = data.X, data.y
n = self.num_samples // 2
groups = np.asarray([0 for _ in range(n)] +
[1 for _ in range(self.num_samples - n)])
dataset_train, dataset_valid = cv_split_cls(GroupKFold(n_splits=2))(
data, groups=groups)
X_train, y_train = data_from_dataset(dataset_train)
X_valid, y_valid = data_from_dataset(dataset_valid)
assert np.allclose(X[:n], X_train)
assert np.allclose(y[:n], y_train)
assert np.allclose(X[n:], X_valid)
assert np.allclose(y[n:], y_valid)
def test_group_kfold(self, cv_split_cls, data):
from sklearn.model_selection import GroupKFold
X, y = data.X, data.y
n = self.num_samples // 2
groups = np.asarray(
[0 for _ in range(n)] + [1 for _ in range(self.num_samples - n)])
dataset_train, dataset_valid = cv_split_cls(
GroupKFold(n_splits=2))(data, groups=groups)
X_train, y_train = data_from_dataset(dataset_train)
X_valid, y_valid = data_from_dataset(dataset_valid)
assert np.allclose(X[:n], X_train)
assert np.allclose(y[:n], y_train)
assert np.allclose(X[n:], X_valid)
assert np.allclose(y[n:], y_valid)
def test_shuffle_split_reproducible_with_random_state(
self, cv_split_cls, dataset_cls):
n = self.num_samples
X, y = np.random.random((n, 10)), np.random.randint(0, 10, size=n)
cv = cv_split_cls(0.2, stratified=False)
dst0, dsv0 = cv(dataset_cls(X, y))
dst1, dsv1 = cv(dataset_cls(X, y))
Xt0, yt0 = data_from_dataset(dst0)
Xv0, yv0 = data_from_dataset(dsv0)
Xt1, yt1 = data_from_dataset(dst1)
Xv1, yv1 = data_from_dataset(dsv1)
assert not np.allclose(Xt0, Xt1)
assert not np.allclose(Xv0, Xv1)
assert not np.allclose(yt0, yt1)
assert not np.allclose(yv0, yv1)
def get_test_data(self, dataset_train, dataset_valid):
"""Return data needed to perform scoring.
This is a convenience method that handles picking of
train/valid, different types of input data, use of cache,
etc. for you.
Parameters
----------
dataset_train
Incoming training data or dataset.
dataset_valid
Incoming validation data or dataset.
Returns
-------
X_test
Input data used for making the prediction.
y_test
Target ground truth. If caching was enabled, return cached
y_test.
y_pred : list
The predicted targets. If caching was disabled, the list is
empty. If caching was enabled, the list contains the batches
of the predictions. It may thus be necessary to concatenate
the output before working with it:
``y_pred = np.concatenate(y_pred)``
"""
dataset = dataset_train if self.on_train else dataset_valid
if self.use_caching:
X_test = dataset
y_pred = self.y_preds_
y_test = [self.target_extractor(y) for y in self.y_trues_]
# In case of y=None we will not have gathered any samples.
# We expect the scoring function to deal with y_test=None.
y_test = np.concatenate(y_test) if y_test else None
return X_test, y_test, y_pred
if is_skorch_dataset(dataset):
X_test, y_test = data_from_dataset(
dataset,
X_indexing=self.X_indexing_,
y_indexing=self.y_indexing_,
)
else:
X_test, y_test = dataset, None
if y_test is not None:
# We allow y_test to be None but the scoring function has
# to be able to deal with it (i.e. called without y_test).
y_test = self.target_extractor(y_test)
return X_test, y_test, []
def test_shuffle_split_reproducible_with_random_state(
self, cv_split_cls, dataset_cls):
n = self.num_samples
X, y = np.random.random((n, 10)), np.random.randint(0, 10, size=n)
cv = cv_split_cls(0.2, stratified=False)
dst0, dsv0 = cv(dataset_cls(X, y))
dst1, dsv1 = cv(dataset_cls(X, y))
Xt0, yt0 = data_from_dataset(dst0)
Xv0, yv0 = data_from_dataset(dsv0)
Xt1, yt1 = data_from_dataset(dst1)
Xv1, yv1 = data_from_dataset(dsv1)
assert not np.allclose(Xt0, Xt1)
assert not np.allclose(Xv0, Xv1)
assert not np.allclose(yt0, yt1)
assert not np.allclose(yv0, yv1)
def on_epoch_end(self, net, dataset_train, dataset_valid, **kwargs):
EpochScoring.on_epoch_end(self, net, dataset_train, dataset_valid)
X_test, y_test = data_from_dataset(dataset_valid)
y_pred = net.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
sample_num = np.sum(cm, axis=1)
sample_num = np.tile(sample_num, (sample_num.size, 1))
cm = cm * (1 / np.transpose(sample_num))
cm = np.round(cm * 100)
cm1 = cm.astype(np.int8)
history = net.history
history.record("confusion_matrix", cm1)
def on_epoch_end(
self,
net,
dataset_train,
dataset_valid,
**kwargs):
dataset = dataset_train if self.on_train else dataset_valid
if self.use_caching:
X_test = dataset
y_pred = self.y_preds_
y_test = [self.target_extractor(y) for y in self.y_trues_]
# In case of y=None we will not have gathered any samples.
# We expect the scoring function to deal with y_test=None.
y_test = np.concatenate(y_test) if y_test else None
else:
if is_skorch_dataset(dataset):
X_test, y_test = data_from_dataset(dataset)
else:
X_test, y_test = dataset, None
y_pred = []
if y_test is not None:
# We allow y_test to be None but the scoring function has
# to be able to deal with it (i.e. called without y_test).
y_test = self.target_extractor(y_test)
if X_test is None:
return
with cache_net_infer(net, self.use_caching, y_pred) as cached_net:
current_score = self._scoring(cached_net, X_test, y_test)
cached_net.history.record(self.name_, current_score)
is_best = self._is_best_score(current_score)
if is_best is None:
return
cached_net.history.record(self.name_ + '_best', is_best)
if is_best:
self.best_score_ = current_score
def on_epoch_end(self, net, dataset_train, dataset_valid, **kwargs):
dataset = dataset_train if self.on_train else dataset_valid
if self.use_caching:
X_test = dataset
y_pred = self.y_preds_
y_test = [self.target_extractor(y) for y in self.y_trues_]
# In case of y=None we will not have gathered any samples.
# We expect the scoring function to deal with y_test=None.
y_test = np.concatenate(y_test) if y_test else None
else:
if is_skorch_dataset(dataset):
X_test, y_test = data_from_dataset(
dataset,
X_indexing=self.X_indexing_,
y_indexing=self.y_indexing_,
)
else:
X_test, y_test = dataset, None
y_pred = []
if y_test is not None:
# We allow y_test to be None but the scoring function has
# to be able to deal with it (i.e. called without y_test).
y_test = self.target_extractor(y_test)
if X_test is None:
return
with cache_net_infer(net, self.use_caching, y_pred) as cached_net:
current_score = self._scoring(cached_net, X_test, y_test)
cached_net.history.record(self.name_, current_score)
is_best = self._is_best_score(current_score)
if is_best is None:
return
cached_net.history.record(self.name_ + '_best', bool(is_best))
if is_best:
self.best_score_ = current_score
def test_with_skorch_ds(self, data_from_dataset, data, skorch_ds):
X, y = data_from_dataset(skorch_ds)
assert (X == data[0]).all()
assert (y == data[1]).all()
def test_with_other_ds(self, data_from_dataset, other_ds):
with pytest.raises(AttributeError):
data_from_dataset(other_ds)
def test_with_dict_data(self, data_from_dataset, data, subset):
subset.dataset.X = {'X': subset.dataset.X}
X, y = data_from_dataset(subset)
assert (X['X'] == data[0][[1, 3]]).all()
assert (y == data[1][[1, 3]]).all()
def test_subset_with_y_none(self, data_from_dataset, data, subset):
subset.dataset.y = None
X, y = data_from_dataset(subset)
assert (X == data[0][[1, 3]]).all()
assert y is None
def test_with_subset_subset(self, data_from_dataset, data, subset_subset):
X, y = data_from_dataset(subset_subset)
assert (X == data[0][1]).all()
assert (y == data[1][1]).all()
def test_with_skorch_ds(self, data_from_dataset, data, skorch_ds):
X, y = data_from_dataset(skorch_ds)
assert (X == data[0]).all()
assert (y == data[1]).all()
def test_subset_with_y_none(self, data_from_dataset, data, subset):
subset.dataset.y = None
X, y = data_from_dataset(subset)
assert (X == data[0][[1, 3]]).all()
assert y is None
def test_with_dict_data(self, data_from_dataset, data, subset):
subset.dataset.X = {'X': subset.dataset.X}
X, y = data_from_dataset(subset)
assert (X['X'] == data[0][[1, 3]]).all()
assert (y == data[1][[1, 3]]).all()
def test_with_other_ds(self, data_from_dataset, other_ds):
with pytest.raises(AttributeError):
data_from_dataset(other_ds)
def test_with_subset_subset(self, data_from_dataset, data, subset_subset):
X, y = data_from_dataset(subset_subset)
assert (X == data[0][1]).all()
assert (y == data[1][1]).all()
