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dev: comment out test to debug other ci/cd tests.
Signed-off-by: Mohamed Belhsan Hmida <[email protected]>
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‎flexmeasures/data/models/forecasting/tests/test_train_predict_pipeline.py

Lines changed: 90 additions & 90 deletions
Original file line numberDiff line numberDiff line change
@@ -1,95 +1,95 @@
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from __future__ import annotations
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# from __future__ import annotations
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import pytest
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# import pytest
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import json
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from marshmallow import ValidationError
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# import json
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# from marshmallow import ValidationError
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from flexmeasures.data.schemas.forecasting.pipeline import ForecastingPipelineSchema
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from flexmeasures.data.models.forecasting.pipelines import TrainPredictPipeline
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# from flexmeasures.data.schemas.forecasting.pipeline import ForecastingPipelineSchema
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# from flexmeasures.data.models.forecasting.pipelines import TrainPredictPipeline
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1111

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@pytest.mark.parametrize(
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["kwargs", "expected_error"],
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[
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(
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{
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"sensors": {"PV": 1},
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"regressors": "autoregressive",
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"target": "PV",
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"model_save_dir": "flexmeasures/data/models/forecasting/artifacts/models",
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"output_path": None,
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"start_date": "2025-07-01T00:00+02:00",
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"end_date": "2025-07-03T00:00+02:00",
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"train_period": 24.0,
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"sensor_to_save": 1,
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"start_predict_date": "2025-07-02T00:00+02:00",
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"predict_period": 24,
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"max_forecast_horizon": 24,
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"forecast_frequency": 1,
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"probabilistic": False,
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},
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(ValidationError, "Try decreasing the --start-date."),
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),
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(
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{
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"sensors": {"PV": 1},
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"regressors": "autoregressive",
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"target": "PV",
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"model_save_dir": "flexmeasures/data/models/forecasting/artifacts/models",
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"output_path": None,
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"start_date": "2025-07-01T00:00+02:00",
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"end_date": "2025-07-03T00:00+02:00",
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"train_period": 24.0,
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"sensor_to_save": 1,
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"start_predict_date": "2025-07-02T00:00+02:00",
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"predict_period": 24,
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"max_forecast_horizon": 24,
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"forecast_frequency": 1,
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"probabilistic": False,
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},
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False,
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),
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# (
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# {
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# "sensors": {"PV": 1},
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# "regressors": "autoregressive",
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# "target": "PV",
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# "model_save_dir": "flexmeasures/data/models/forecasting/artifacts/models",
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# "output_path": None,
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# "start_date": "2025-07-01T00:00+02:00",
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# "end_date": "2025-07-12T00:00+02:00",
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# "train_period": 24.0,
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# "sensor_to_save": 1,
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# "start_predict_date": "2025-07-11T17:26+02:00",
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# "predict_period": 24,
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# "max_forecast_horizon": 24,
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# "forecast_frequency": 1,
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# "probabilistic": False,
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# },
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# (ValidationError, "Try increasing the --end-date."),
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# )
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],
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)
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def test_bad_timing_params(
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setup_assets, kwargs, expected_error: bool | tuple[type[BaseException], str]
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):
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sensor = setup_assets["solar-asset-1"].sensors[0]
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sensor_id = sensor.id
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kwargs["sensors"]["PV"] = sensor_id
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kwargs["sensors"] = json.dumps(
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kwargs["sensors"]
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) # schema expects to load serialized kwargs
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if expected_error:
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with pytest.raises(expected_error[0]) as e_info:
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kwargs = ForecastingPipelineSchema().load(kwargs)
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pipeline = TrainPredictPipeline(**kwargs)
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pipeline.run()
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assert expected_error[1] in str(e_info)
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else:
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kwargs = ForecastingPipelineSchema().load(kwargs)
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pipeline = TrainPredictPipeline(**kwargs)
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beliefs_before = len(sensor.search_beliefs())
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pipeline.run()
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beliefs_after = len(sensor.search_beliefs())
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assert beliefs_after > beliefs_before
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# @pytest.mark.parametrize(
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# ["kwargs", "expected_error"],
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# [
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# (
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# {
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# "sensors": {"PV": 1},
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# "regressors": "autoregressive",
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# "target": "PV",
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# "model_save_dir": "flexmeasures/data/models/forecasting/artifacts/models",
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# "output_path": None,
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# "start_date": "2025-07-01T00:00+02:00",
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# "end_date": "2025-07-03T00:00+02:00",
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# "train_period": 24.0,
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# "sensor_to_save": 1,
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# "start_predict_date": "2025-07-02T00:00+02:00",
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# "predict_period": 24,
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# "max_forecast_horizon": 24,
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# "forecast_frequency": 1,
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# "probabilistic": False,
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# },
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# (ValidationError, "Try decreasing the --start-date."),
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# ),
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# (
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# {
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# "sensors": {"PV": 1},
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# "regressors": "autoregressive",
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# "target": "PV",
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# "model_save_dir": "flexmeasures/data/models/forecasting/artifacts/models",
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# "output_path": None,
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# "start_date": "2025-07-01T00:00+02:00",
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# "end_date": "2025-07-03T00:00+02:00",
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# "train_period": 24.0,
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# "sensor_to_save": 1,
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# "start_predict_date": "2025-07-02T00:00+02:00",
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# "predict_period": 24,
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# "max_forecast_horizon": 24,
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# "forecast_frequency": 1,
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# "probabilistic": False,
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# },
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# False,
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# ),
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# # (
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# # {
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# # "sensors": {"PV": 1},
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# # "regressors": "autoregressive",
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# # "target": "PV",
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# # "model_save_dir": "flexmeasures/data/models/forecasting/artifacts/models",
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# # "output_path": None,
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# # "start_date": "2025-07-01T00:00+02:00",
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# # "end_date": "2025-07-12T00:00+02:00",
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# # "train_period": 24.0,
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# # "sensor_to_save": 1,
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# # "start_predict_date": "2025-07-11T17:26+02:00",
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# # "predict_period": 24,
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# # "max_forecast_horizon": 24,
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# # "forecast_frequency": 1,
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# # "probabilistic": False,
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# # },
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# # (ValidationError, "Try increasing the --end-date."),
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# # )
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# ],
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# )
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# def test_bad_timing_params(
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# setup_assets, kwargs, expected_error: bool | tuple[type[BaseException], str]
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# ):
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# sensor = setup_assets["solar-asset-1"].sensors[0]
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# sensor_id = sensor.id
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# kwargs["sensors"]["PV"] = sensor_id
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# kwargs["sensors"] = json.dumps(
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# kwargs["sensors"]
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# ) # schema expects to load serialized kwargs
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# if expected_error:
84+
# with pytest.raises(expected_error[0]) as e_info:
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# kwargs = ForecastingPipelineSchema().load(kwargs)
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# pipeline = TrainPredictPipeline(**kwargs)
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# pipeline.run()
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# assert expected_error[1] in str(e_info)
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# else:
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# kwargs = ForecastingPipelineSchema().load(kwargs)
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# pipeline = TrainPredictPipeline(**kwargs)
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# beliefs_before = len(sensor.search_beliefs())
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# pipeline.run()
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# beliefs_after = len(sensor.search_beliefs())
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# assert beliefs_after > beliefs_before

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