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 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_data_types(self, is_skorch_dataset, input_data, expected):
assert is_skorch_dataset(input_data) == expected
def test_data_types(self, is_skorch_dataset, input_data, expected):
assert is_skorch_dataset(input_data) == expected
