def predict(cls, experiment, sample):
name = experiment.name
exp_locator = Locator(experiment.id, experiment.train_data, experiment.test_data)
if 'LR_' in name:
lr_dataset_maker = LRPimaIndiansDatasetMaker(exp_locator)
X = lr_dataset_maker.make_one_sample(sample)
logistic_regression = DataLoader.load(exp_locator.get_model_file_path())
prediction = logistic_regression.predict(X)
prediction = prediction.tolist()
return prediction
def test(cls, experiment):
name = experiment.name
exp_locator = Locator(experiment.id, experiment.train_data, experiment.test_data)
if 'LR_' in name:
lr_dataset_maker = LRPimaIndiansDatasetMaker(exp_locator)
X, y = lr_dataset_maker.make_test_dataset()
logistic_regression = DataLoader.load(exp_locator.get_model_file_path())
test_result = logistic_regression.test(X, y)
exp_result = json.loads(experiment.result)
exp_result.append(test_result)
experiment.result = json.dumps(exp_result)
return experiment
raise Exception("No valid Experiment Name to train Experiment")
def is_experiment_trained(cls, experiment):
exp_locator = Locator(experiment.id, experiment.train_data, experiment.test_data)
if os.path.exists(exp_locator.get_model_file_path()):
return True
else:
return False