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handling empty data cases in testing piipeline

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Khalim Conn-Kowlessar 2024-02-12 16:03:51 +00:00
parent 61488b1aea
commit 7c109ebf5d
1 changed files with 45 additions and 36 deletions
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81
etl/testing_data/sap_model_simulation.py
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@ -232,10 +232,13 @@ def app():
population["is_solid"] & (population["floor_insulation_thickness"] == "none") population["is_solid"] & (population["floor_insulation_thickness"] == "none")
] ]
solid_floor_uvalues = solid_floor_sample["floor_thermal_transmittance"].quantile([0.25, 0.5, 0.75]).values
solid_floor_uvalues = {v for v in solid_floor_uvalues if not pd.isnull(v)}
# We have many different values of u-value for solid floors, we we'll take a sample at the 25%, 50% and 75% # We have many different values of u-value for solid floors, we we'll take a sample at the 25%, 50% and 75%
# values # values
# We must take a value that is in one of the unique values for floor_thermal_transmittance # We must take a value that is in one of the unique values for floor_thermal_transmittance
for uvalue in solid_floor_sample["floor_thermal_transmittance"].quantile([0.25, 0.5, 0.75]).values: for uvalue in solid_floor_uvalues:
nearest_value = solid_floor_sample['floor_thermal_transmittance'].sub(uvalue).abs().idxmin() nearest_value = solid_floor_sample['floor_thermal_transmittance'].sub(uvalue).abs().idxmin()
nearest_row = solid_floor_sample.loc[[nearest_value]].sample(1) nearest_row = solid_floor_sample.loc[[nearest_value]].sample(1)
@ -258,8 +261,13 @@ def app():
population["is_suspended"] & (population["floor_insulation_thickness"] == "none") population["is_suspended"] & (population["floor_insulation_thickness"] == "none")
] ]
suspended_floor_uvalues = suspended_floor_sample["floor_thermal_transmittance"].quantile(
[0.25, 0.5, 0.75]
).values
suspended_floor_uvalues = {v for v in suspended_floor_uvalues if not pd.isnull(v)}
# We take the same approach as for solid floors # We take the same approach as for solid floors
for uvalue in suspended_floor_sample["floor_thermal_transmittance"].quantile([0.25, 0.5, 0.75]).values: for uvalue in suspended_floor_uvalues:
nearest_value = suspended_floor_sample['floor_thermal_transmittance'].sub(uvalue).abs().idxmin() nearest_value = suspended_floor_sample['floor_thermal_transmittance'].sub(uvalue).abs().idxmin()
nearest_row = suspended_floor_sample.loc[[nearest_value]].sample(1) nearest_row = suspended_floor_sample.loc[[nearest_value]].sample(1)
@ -282,14 +290,7 @@ def app():
(population["glazing_type"] == "single") (population["glazing_type"] == "single")
] ]
# We take multiple values for multi_glaze_proportion_starting. We definitely need zero, but then we also if not single_glazing_sample.empty:
# take the 25%, 50% and 75% values
multi_glaze_values = [0] + list(
single_glazing_sample["multi_glaze_proportion_starting"].quantile([0.25, 0.5, 0.75]).values
)
multi_glaze_values = set(multi_glaze_values)
for value in multi_glaze_values:
row = single_glazing_sample[ row = single_glazing_sample[
single_glazing_sample["multi_glaze_proportion_starting"] == value single_glazing_sample["multi_glaze_proportion_starting"] == value
].sample(1) ].sample(1)
@ -409,40 +410,48 @@ def app():
# We only recommend solar for properties that have flat or pitched roofs, and no existing solar # We only recommend solar for properties that have flat or pitched roofs, and no existing solar
pitched_roof_no_solar = population[ pitched_roof_no_solar = population[
(population["is_pitched"]) & (population["photo_supply_starting"] == 0) (population["is_pitched"]) & (population["photo_supply_starting"] == 0)
].sample(1) ]
if not pitched_roof_no_solar.empty:
pitched_roof_no_solar = pitched_roof_no_solar.sample(1)
flat_roof_no_solar = population[ flat_roof_no_solar = population[
(population["is_flat"]) & (population["photo_supply_starting"] == 0) (population["is_flat"]) & (population["photo_supply_starting"] == 0)
].sample(1) ]
if not flat_roof_no_solar.empty:
flat_roof_no_solar = flat_roof_no_solar.sample(1)
# We simulate 30%, 40% and 50% coverage # We simulate 30%, 40% and 50% coverage
for coverage in [30, 40, 50]: for coverage in [30, 40, 50]:
solar_simulation_pitched = Property.create_recommendation_scoring_data(
property_id=pitched_roof_no_solar["uprn"].values[0],
recommendation_record=pitched_roof_no_solar.copy().to_dict("records")[0],
recommendation={
"recommendation_id": "solar_pv",
"type": "solar_pv",
"new_u_value": None, # This doesn't matter at the moment
"parts": [],
"photo_supply": coverage
}
)
solar_simulation_flat = Property.create_recommendation_scoring_data( if not pitched_roof_no_solar.empty:
property_id=flat_roof_no_solar["uprn"].values[0], solar_simulation_pitched = Property.create_recommendation_scoring_data(
recommendation_record=flat_roof_no_solar.copy().to_dict("records")[0], property_id=pitched_roof_no_solar["uprn"].values[0],
recommendation={ recommendation_record=pitched_roof_no_solar.copy().to_dict("records")[0],
"recommendation_id": "solar_pv", recommendation={
"type": "solar_pv", "recommendation_id": "solar_pv",
"new_u_value": None, # This doesn't matter at the moment "type": "solar_pv",
"parts": [], "new_u_value": None, # This doesn't matter at the moment
"photo_supply": coverage "parts": [],
} "photo_supply": coverage
) }
)
pitched_roof_solar.append(solar_simulation_pitched)
pitched_roof_solar.append(solar_simulation_pitched) if not flat_roof_no_solar.empty:
flat_roof_solar.append(solar_simulation_flat) solar_simulation_flat = Property.create_recommendation_scoring_data(
property_id=flat_roof_no_solar["uprn"].values[0],
recommendation_record=flat_roof_no_solar.copy().to_dict("records")[0],
recommendation={
"recommendation_id": "solar_pv",
"type": "solar_pv",
"new_u_value": None, # This doesn't matter at the moment
"parts": [],
"photo_supply": coverage
}
)
flat_roof_solar.append(solar_simulation_flat)
# We store all of this data in s3, as it is # We store all of this data in s3, as it is
save_data_to_s3( save_data_to_s3(