def test_results_normal(self):
result = Result(self.cardinality)
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
result_dump = pickle.dumps({
"Result": {
"1": {
"Test": result,
"Train": result
},
"2": {
"Test": result,
"Train": result
}
}
})
mockOpen = mock_open(read_data=result_dump)
with patch('builtins.open', mockOpen):
results = Results(path_model="model/", name_model="Test")
results.load_files()
results.compute_results(condition="Test")
self.assertEqual(results.global_TP, 4)
self.assertEqual(results.global_FP, 4)
self.assertEqual(results.global_FN, 4)
self.assertEqual(results.macro_precision, 0.50)
self.assertEqual(results.macro_recall, 0.50)
self.assertTrue(math.isclose(results.micro_recall, 1 / 3))
self.assertTrue(math.isclose(results.micro_precision, 1 / 3))
results.print_results()
def test_update(self):
result = Result(self.cardinality)
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
self.assertEqual(result.conf_matrix[0, 1], 1)
self.assertEqual(result.conf_matrix[1, 0], 1)
self.assertEqual(result.conf_matrix[1, 1], 1)
self.assertEqual(result.conf_matrix[0, 0], 1)
def setUp(self):
self.lock = torch.multiprocessing.get_context('spawn').Lock()
self.model = LSTMLayer(num_classes=5)
cardinality = Cardinality(3, "", "")
cardinality.list_classes = [1,1,1,2,2,3,4,5,6]
cardinality.counter= {1:10, 2:100, 3:100, 4:100, 6:1000, 5:1000}
cardinality.compute_position()
self.result = Result(cardinality)
def test_no_class(self):
result = Result(self.cardinality)
result.number_of_classes = 0
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
result.computing_result()
self.assertEqual(result.micro_precision, 0)
self.assertEqual(result.micro_recall, 0)
def test_compute(self):
result = Result(self.cardinality)
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
result.computing_result()
self.assertEqual(result.global_TP, 2)
self.assertEqual(result.global_FP, 2)
self.assertEqual(result.global_FN, 2)
self.assertEqual(result.macro_precision, 0.50)
self.assertEqual(result.macro_recall, 0.50)
self.assertTrue(math.isclose(result.micro_recall, 1 / 3))
self.assertTrue(math.isclose(result.micro_precision, 1 / 3))
def test(self,
validation_split=0.6,
subsample=False,
subsample_split=0.01):
"""Test the model
Args:
validation_split (float, optional): ratio between testing and learning set. Defaults to 0.6.
subsample (bool, optional): if False, use all the available data, if True, use only a ratio of the data (subsample_split*data). Defaults to False.
subsample_split (float, optional): ratio of the data to use. Defaults to 0.01.
"""
dataloader_test = self.create_dataloader(
validation_split=validation_split,
condition="Test",
subsample=subsample,
subsample_split=0.01)
result = Result(self.dataset, condition="Test", subsample=subsample)
if self.model == -1:
self.load_model()
self.model.eval()
self.conf_matrix = torch.zeros(self.dataset.number_of_classes,
self.dataset.number_of_classes)
for index_batch, batch in enumerate(dataloader_test):
label = batch['output']
input_data = batch['input'].to(self.device)
prediction = self.model(input_data)
result.update(prediction, label)
if index_batch % self.batch_result == 0:
result.computing_result(reinit=False,
progress=index_batch /
len(dataloader_test))
self.model.train()
self.saver.save(model=self.model, result=result, condition="Test")
result.computing_result(reinit=True, progress=1)
self.stopping_condition.update(result.microf1)
self.stopping_condition.stop()
def test_print(self):
result = Result(self.cardinality, "Test")
result.number_of_classes = 0
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
result.computing_result()
result = Result(self.cardinality)
result.number_of_classes = 0
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
result.computing_result()
def test_creating(self):
result = Result(self.cardinality)
self.assertEqual(result.number_of_classes, 3)
def train(self, validation_split=0.6, resuming=False):
"""Train the model
Args:
validation_split (float, optional): ratio between testing and learning set. Defaults to 0.6.
resuming (bool, optional): resume the learning from a previous step. Not implemented yet. Defaults to False.
"""
# Create the dataloader
dataloader_train = self.create_dataloader(
validation_split=validation_split, condition="train")
if resuming:
self.load_model()
else:
self.model = LSTMLayer(num_classes=self.dataset.number_of_classes,
batch_size=self.batch_size).to(self.device)
# Create the results
result = Result(self.dataset, condition="Train")
optimizer = optim.Adam(self.model.parameters())
loss_fn = nn.CrossEntropyLoss()
self.model.train()
logger.info("Cardinality: " + str(self.cardinality) +
" Starting the learning step")
# Start the learning
while not self.stopping_condition.stop():
for index_batch, batch in enumerate(dataloader_train):
optimizer.zero_grad()
label = batch['output']
input_data = batch['input'].to(self.device)
prediction = self.model(input_data)
loss = loss_fn(prediction, label.to(self.device))
loss.backward()
optimizer.step()
result.update(prediction, label)
# Compute the results each 2000 batchs.
if index_batch % self.batch_result == 0 and index_batch != 0:
result.computing_result(progress=index_batch /
len(dataloader_train))
self.saver.save(model=self.model,
result=result,
condition="temp")
if not self.exlude_test:
# Test only on a subsample
self.test(subsample=True, subsample_split=0.1)
print(self.stopping_condition)
# Test if we stop only for the timer method at each batch
if self.stopping_condition.method == "timer" and self.stopping_condition.stop(
):
logger.debug("[Stopping] Cardinality: " +
str(self.cardinality) + " " +
str(self.stopping_condition) +
" stopping learning step.")
break
# At the end of one epoch, use the all testing test and update the condition
if not self.exlude_test:
self.test()
result.computing_result(reinit=True, progress=1)
if self.stopping_condition.stop():
logger.debug("[Stopping] Cardinality: " +
str(self.cardinality) + " " +
str(self.stopping_condition) +
" stopping learning step.")
self.saver.save(model=self.model, result=result, condition="Train")