def get_prediction_job(cls, pid: Union[int, str]):
with open(cls.DATA_FILES_FOLDER / "prediction_jobs.json", "r") as fh:
prediction_jobs = json.load(fh, object_hook=prediction_job_decoder)
out_dict = prediction_jobs[str(pid)]
# Change the typ column to forecast_type normally done after query
out_dict["forecast_type"] = out_dict.pop("typ")
return PredictionJobDataClass(**out_dict)
def get_prediction_jobs(cls):
with open(cls.DATA_FILES_FOLDER / "prediction_jobs.json", "r") as fh:
prediction_jobs = json.load(fh, object_hook=prediction_job_decoder)
prediction_jobs_list = []
for v in prediction_jobs.values():
# Change the typ column to forecast_type normally done after query
v["forecast_type"] = v.pop("typ")
prediction_jobs_list.append(PredictionJobDataClass(**v))
return prediction_jobs_list
def _build_prediction_job(self, pj_id, depends_on=None):
return PredictionJobDataClass(
id=pj_id,
depends_on=depends_on,
model="",
forecast_type="",
train_components=False,
name="",
lat=0,
lon=0,
resolution_minutes=0,
horizon_minutes=0,
)
def test_prediction_job_from_dict_with_wind_columns(self):
# Arrange
pj_dict = {
"id": 307,
"turbine_type": "test",
"n_turbines": 3.0,
"hub_height": 20.0,
"model": "xgb",
"model_type_group": "xgb",
"horizon_minutes": 2880,
"resolution_minutes": 15,
"train_components": 1,
"name": "Neerijnen",
"lat": 51.8336647,
"lon": 5.2137814,
"sid": "NrynRS_10-G_V12_P",
"created": "2019-04-05 12:08:23",
"description":
"NrynRS_10-G_V12_P+NrynRS_10-G_V13_P+NrynRS_10-G_V14_P+NrynRS_10-G_V15_P+NrynRS_10-G_V16_P+NrynRS_10-G_V17_P+NrynRS_10-G_V18_P+NrynRS_10-G_V20_P+NrynRS_10-G_V21_P+NrynRS_10-G_V22_P+NrynRS_10-G_V23_P+NrynRS_10-G_V24_P+NrynRS_10-G_V25_P",
"quantiles": [0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95],
"hyper_params": {
"subsample": 0.9650102355823993,
"min_child_weight": 3,
"max_depth": 6,
"gamma": 0.1313691782115394,
"colsample_bytree": 0.8206844265155975,
"silent": 1,
"objective": "reg:squarederror",
"eta": 0.010025843216782565,
"training_period_days": 90,
},
"feature_names": [
"clearSky_dlf",
"clearSky_ulf",
],
"forecast_type": "demand",
}
# Assert
pj = PredictionJobDataClass(**pj_dict)
# Act
self.assertIsInstance(pj, PredictionJobDataClass)
self.assertEqual(pj["id"], 307)
self.assertEqual(pj["turbine_type"], "test")
self.assertEqual(pj["n_turbines"], 3.0)
self.assertEqual(pj["hub_height"], 20.0)