def make_predictions(): #We will make predictions for those experiments, which are already finished, but don't have the csv file with predictions
experiments = projects.Project(Path(__file__).parent.parent).experiments()
for exp in experiments:
res_file = os.path.join(exp.path, exp.name() + '_result.csv')
if exp.isCompleted() and not os.path.exists(res_file):
preds = generic.parse(exp.path).predictions('test')
preds.dump(res_file)
def basic_test(experiment_name: str):
pr = projects.Project(os.path.join(fl, "../examples"))
exp = pr.byName(experiment_name)
tasks = exp.fit()
executor = parralel.get_executor(1, 1)
executor.execute(tasks)
return exp.result(False, True)
def test_basic_network(self):
pr = projects.Project(os.path.join(fl, "project"))
exp = pr.byName("exp01")
tasks = exp.fit()
executor = parralel.get_executor(1, 1)
executor.execute(tasks)
r = exp.result()
self.assertGreaterEqual(r, 0, "Result should be greater then zero")
self.assertTrue(isinstance(r, float), "result should be float")
print(r)
pass
def make_predictions():
experiments = projects.Project(Path(__file__).parent.parent).experiments()
for exp in experiments:
if exp.isCompleted():
file_path=os.path.join(context.get_current_project_data_path(),"rus.vocab")
vocabulary=utils.load(file_path)
preds = generic.parse(exp.path).predictions('test')
for item in preds:
rootItem = item.rootItem()
sentence = ''
for indices in item.prediction:
sentence = sentence + " " + vocabulary.i2w[np.argmax(indices)]
print(rootItem.x + " " + sentence)
def project(self, id):
return projects.Project(os.path.join(self.root, id))
def __init__(self, path):
self.path = path
self.project = projects.Project(path)
self.project.get_visualizers()
