def setUpClass(cls):
metamap = MetaMap(
metamap_path=
"/home/share/programs/metamap/2016/public_mm/bin/metamap",
cache_output=False)
cls.pipeline = ClinicalPipeline(
metamap) # Will fail as MetaMap isn't installed
def test_with_metamap(self):
"""
Constructs a model that memorizes an entity, predicts it on same file, writes to ann
:return:
"""
train_loader = DataLoader(self.train_dir)
test_loader = DataLoader(self.test_dir)
metamap = MetaMap(metamap_path="/home/share/programs/metamap/2016/public_mm/bin/metamap",
cache_output=False)
train_loader.metamap(metamap)
test_loader.metamap(metamap)
pipeline = ClinicalPipeline(metamap, entities=['Strength'])
learner = Learner(pipeline, train_loader)
model = learner.train()
predictor = Predictor(pipeline, test_loader, model=model)
predictor.predict()
with open(predictor.prediction_directory+"predict_test.ann") as f:
self.assertEqual(f.read(), "T1 Strength 7 11 5 mg\n")
def test_with_metamap(self):
loader = DataLoader(self.test_dir)
metamap = MetaMap(
metamap_path=
"/home/share/programs/metamap/2016/public_mm/bin/metamap")
loader.metamap(metamap) #pre-cache metamap files
pipeline = ClinicalPipeline(metamap, entities=['Strength'])
learner = Learner(pipeline, loader)
model = learner.train()
self.assertIsInstance(learner, Learner)
def test_fit_with_clinical_pipeline(self):
"""
Loads in training data and uses it to fit a model using the Clinical Pipeline
:return:
"""
train_loader = DataLoader(self.train_dir)
metamap = MetaMap(
metamap_path=
"/home/share/programs/metamap/2016/public_mm/bin/metamap",
cache_output=False)
train_loader.metamap(metamap)
pipeline = ClinicalPipeline(metamap, entities=['Strength'])
model = Model(pipeline)
model.fit(train_loader)
self.assertIsInstance(model, Model)
self.assertIsNot(model.model, None)
def test_prediction_with_clinical_pipeline(self):
"""
Constructs a model that memorizes an entity, predicts it on same file, writes to ann
:return:
"""
train_loader = DataLoader(self.train_dir)
test_loader = DataLoader(self.test_dir)
metamap = MetaMap(
metamap_path=
"/home/share/programs/metamap/2016/public_mm/bin/metamap",
cache_output=False)
train_loader.metamap(metamap)
test_loader.metamap(metamap)
pipeline = ClinicalPipeline(metamap, entities=['Strength'])
model = Model(pipeline)
model.fit(train_loader)
model.predict(test_loader)
with open(self.test_dir + "/predictions/" + "predict_test.ann") as f:
self.assertEqual(f.read(), "T1 Strength 7 11 5 mg\n")
def setUpClass(cls):
cls.pipeline = ClinicalPipeline(
) # Will fail as MetaMap isn't installed
def setUpClass(cls):
cls.pipeline = ClinicalPipeline()