def test_set_state_get_state():
classifier = module.DiseaseClassifier(components={'in_width': 100, 'out_width': 55})
state = classifier.get_state()
new_classifier = module.DiseaseClassifier(components={'in_width': 100, 'out_width': 55})
assert not (classifier.state_dict()['classification_layer.weight'] == new_classifier.state_dict()['classification_layer.weight']).all()
new_state = new_classifier.get_state()
new_classifier.set_state(state, reset=False)
assert (classifier.state_dict()['classification_layer.weight'] == new_classifier.state_dict()['classification_layer.weight']).all()
def test_roc_trainer():
data = get_test_examples()
metanet = module.MetaphlanNet(
components={'widths': [12365, 6000, 2000, 55]})
classifier = module.DiseaseClassifier(components={
"in_width": 55,
"out_width": 49
},
input=metanet)
if torch.cuda.is_available():
data.cuda()
metanet.cuda()
classifier.cuda()
bce = nn.BCELoss()
def loss(batch):
return bce(batch['predictions'], batch['label'])
trainer = hyperfactory.roc_trainer(classifier,
loss,
components={
"in_width": 55,
"out_width": 49
},
input=metanet)
evaluator = trainer(Message({'roc_bias': torch.ones(49)}))
assert hasattr(evaluator, 'run')
evaluator.run(data)
def trainer(parameters: dict, max_epochs: int = 15):
converted_params = convert_keys_to_variables(parameters)
embedder = module.MetaphlanNet(converted_params['widths'])
classifier = module.DiseaseClassifier(converted_params['in_width'],
converted_params['out_width'])
learning_rate = converted_params['learning_rate']
engine = module.get_trainer(embedder, classifier)
train_loader = BatchingPipe(inputs=train_dataset)
engine.run(train_loader, max_epochs=max_epochs)
eval_engine = module.get_evaluator(embedder, classifier, attach=False)
engine = None
return eval_engine
def test_DiseaseClassifier():
metanet = module.MetaphlanNet(components={'widths': [12365, 6000, 2000, 100]})
classifier = module.DiseaseClassifier(components={'in_width': 100, 'out_width': 55})
data = get_test_examples()
if torch.cuda.is_available():
metanet.cuda()
classifier.cuda()
data.cuda()
output = classifier(metanet((data[2])))
assert len(output['embeddings'][0]) == 100
output = classifier(metanet(data[3:10]))
assert output['predictions'].shape == torch.Size([7, 55])
def test_roc_bias_generator():
in_width = 50
out_width = 55
classifier = module.DiseaseClassifier(components={
'in_width': in_width,
'out_width': out_width
})
bias_generator = hyperfactory.roc_bias_generator(classifier)
new_bias = bias_generator(None, None)
assert type(new_bias) is Message
assert 'roc_bias' in new_bias
assert len(new_bias['roc_bias'][0]) == out_width
assert len(new_bias['roc_bias']) == 1
def test_PrevalenceNet():
metanet = module.MetaphlanNet(components={'widths': [12365, 6000, 1000]})
prior = torch.empty(55).uniform_(0,1)
prior = prior / sum(prior)
prevalence = module.Concatenator(input=metanet, components={"in_column": "embeddings", "out_column": "embeddings", "concatenate_column": "prevalence"})
posterior = module.PosteriorNet(input=prevalence, components={'widths': [1055, 100]})
classifier = module.DiseaseClassifier(input=posterior, components={'in_width': 100, 'out_width': 55})
data = get_test_examples()
batch = data[0:10]
batch['prevalence'] = prior.expand(len(batch), len(prior))
if torch.cuda.is_available():
metanet.cuda()
prevalence.cuda()
posterior.cuda()
classifier.cuda()
batch.cuda()
output = classifier(batch)
'phylogenetic_tree.torch'
) # This is the adjacency matrix for the tree in COO format.
conv_net = module.DeepConvNet(components={
"channels": [1, 64, 64, 1],
"edge_index": coo,
"num_nodes": 12365
})
deep_net = module.MetaphlanNet(components={
"widths": [12365, 350],
"in_column": 'embeddings'
},
input=conv_net)
classifier = module.DiseaseClassifier(input=deep_net,
components={
'in_column': 'embeddings',
'in_width': 350,
'out_width': len(study_labels)
})
single_classifier = module.MulticlassDiseaseClassifier(input=classifier,
components={
'in_column':
'embeddings',
'out_column':
'top_prediction'
})
bce = nn.BCELoss(size_average=False)
ce = nn.CrossEntropyLoss()
if torch.cuda.is_available():