def test_model(model_path):
""" Tests results of given model on dataset """
DATA_PATH = 'data/data.csv'
if not os.path.isfile(FILE_PATH):
wget.download(PREPROCESS_URL, FILE_PATH)
dataset = pd.read_csv(DATA_PATH)
dataset['target'] = dataset['target'].astype(float)
dataset['time_idx'] = dataset['time_idx'].astype(int)
time_series = TimeSeriesDataSet.load('models/dataset_time_set')
validation = TimeSeriesDataSet.from_dataset(time_series, dataset)
all_dataloader = validation.to_dataloader(train=False, num_workers=0)
model = TemporalFusionTransformer.load_from_checkpoint(model_path)
actuals = torch.cat([y[0] for (x, y) in iter(all_dataloader)])
predictions = model.predict(all_dataloader)
print(f'test mape is {((actuals - predictions).abs() / actuals).mean()}')
print(f' max mape {max(((actuals - predictions).abs() / actuals))}')
res = (actuals - predictions).abs() / actuals
print(f' max 99 mape {np.quantile(res, .99)}')
# print("wynik", res)
res = np.array([int(x) for x in res])
def predict(self, data):
""" Transforms data and predicts output based on train model
Parameters: self, list of protocols
Return: list of results for each protocol based on train model
"""
print(data)
self.save_time_series()
dataset = self.prepare_data(data)
time_series = TimeSeriesDataSet.load(self.TIMESERIES_PATH)
validation = TimeSeriesDataSet.from_dataset(time_series, dataset)
val_dataloader = validation.to_dataloader(train=False, num_workers=0)
res = self.model.predict(val_dataloader)
# print("wynik", res)
res = np.array([int(x) for x in res])
return res
