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debugging simulation testing pipeline
This commit is contained in:
Khalim Conn-Kowlessar
parent
a0bbdadd1f
commit
61488b1aea
1 changed files with 92 additions and 12 deletions
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@ -1,12 +1,15 @@
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import json
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import pandas as pd
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from utils.s3 import read_dataframe_from_s3_parquet
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from tqdm import tqdm
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from utils.s3 import read_dataframe_from_s3_parquet, save_data_to_s3
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from backend.Property import Property
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def app():
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dataset = read_dataframe_from_s3_parquet(
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bucket_name="retrofit-datalake-dev",
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file_key="dataset_with0perm_all.parquet"
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bucket_name="retrofit-data-dev",
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file_key="sap_change_model/dataset.parquet"
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)
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thresholds = dataset["total_floor_area_starting"].quantile(
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@ -81,7 +84,9 @@ def app():
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single_glazed_testing_data = []
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partial_double_glazed_testing_data = []
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partial_secondary_glazed_testing_data = []
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for property_config in property_types.itertuples():
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pitched_roof_solar = []
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flat_roof_solar = []
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for property_config in tqdm(property_types.itertuples(), total=property_types.shape[0]):
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# Take a sample row
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population = dataset[
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(dataset["property_type"] == property_config.property_type) &
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@ -101,13 +106,18 @@ def app():
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row = population[
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(population["roof_insulation_thickness"] == "none") &
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(population["is_pitched"])
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].sample(1)
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]
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else:
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row = population[
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(population["roof_insulation_thickness"] == insulation_thickness) &
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(population["is_pitched"])
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].sample(1)
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]
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if row.empty:
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continue
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row = row.sample(1)
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loft_insulation_270mm_simulation = Property.create_recommendation_scoring_data(
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property_id=row["uprn"].values[0],
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@ -164,8 +174,8 @@ def app():
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# Simulated IWI
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internal_wall_insulation_simulation = Property.create_recommendation_scoring_data(
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property_id=row["uprn"].values[0].to_dict("records")[0],
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recommendation_record=row.copy(),
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property_id=row["uprn"].values[0],
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recommendation_record=row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "internal_wall_insulation",
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"type": "internal_wall_insulation",
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@ -333,9 +343,14 @@ def app():
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)
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]
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for value in partial_double_glazing_sample["multi_glaze_proportion_starting"].quantile(
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partial_double_glazed_values = partial_double_glazing_sample["multi_glaze_proportion_starting"].quantile(
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[0.25, 0.5, 0.75]
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).values:
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).values
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# Take non-null values
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partial_double_glazed_values = [v for v in partial_double_glazed_values if not pd.isnull(v)]
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partial_double_glazed_values = set(partial_double_glazed_values)
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for value in partial_double_glazed_values:
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nearest_value = partial_double_glazing_sample['multi_glaze_proportion_starting'].sub(value).abs().idxmin()
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nearest_row = partial_double_glazing_sample.loc[[nearest_value]].sample(1)
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# If we start with partial double glazing, we recommend completing the job
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@ -361,9 +376,14 @@ def app():
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)
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]
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for value in partial_secondary_glazing_sample["multi_glaze_proportion_starting"].quantile(
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partial_secondary_glazed_values = partial_secondary_glazing_sample["multi_glaze_proportion_starting"].quantile(
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[0.25, 0.5, 0.75]
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).values:
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).values
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# Take non-null values
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partial_secondary_glazed_values = [v for v in partial_secondary_glazed_values if not pd.isnull(v)]
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partial_secondary_glazed_values = set(partial_secondary_glazed_values)
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for value in partial_secondary_glazed_values:
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nearest_value = partial_secondary_glazing_sample['multi_glaze_proportion_starting'].sub(
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value).abs().idxmin()
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nearest_row = partial_secondary_glazing_sample.loc[[nearest_value]].sample(1)
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@ -383,3 +403,63 @@ def app():
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)
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partial_secondary_glazed_testing_data.append(secondary_glazing_simulation)
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# 9) Solar PV
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# We only recommend solar for properties that have flat or pitched roofs, and no existing solar
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pitched_roof_no_solar = population[
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(population["is_pitched"]) & (population["photo_supply_starting"] == 0)
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].sample(1)
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flat_roof_no_solar = population[
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(population["is_flat"]) & (population["photo_supply_starting"] == 0)
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].sample(1)
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# We simulate 30%, 40% and 50% coverage
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for coverage in [30, 40, 50]:
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solar_simulation_pitched = Property.create_recommendation_scoring_data(
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property_id=pitched_roof_no_solar["uprn"].values[0],
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recommendation_record=pitched_roof_no_solar.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "solar_pv",
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"type": "solar_pv",
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"new_u_value": None, # This doesn't matter at the moment
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"parts": [],
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"photo_supply": coverage
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}
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)
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solar_simulation_flat = Property.create_recommendation_scoring_data(
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property_id=flat_roof_no_solar["uprn"].values[0],
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recommendation_record=flat_roof_no_solar.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "solar_pv",
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"type": "solar_pv",
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"new_u_value": None, # This doesn't matter at the moment
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"parts": [],
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"photo_supply": coverage
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}
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)
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pitched_roof_solar.append(solar_simulation_pitched)
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flat_roof_solar.append(solar_simulation_flat)
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# We store all of this data in s3, as it is
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save_data_to_s3(
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bucket_name="retrofit-datalake-dev",
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s3_file_name="sap_change_model/loft_insulation_testing_data.parquet",
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data=json.dumps(
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{
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"loft_insulation_testing_data": loft_insulation_testing_data,
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"solid_wall_testing_data": solid_wall_testing_data,
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"cavity_wall_testing_data": cavity_wall_testing_data,
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"solid_floor_testing_data": solid_floor_testing_data,
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"suspended_floor_testing_data": suspended_floor_testing_data,
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"single_glazed_testing_data": single_glazed_testing_data,
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"partial_double_glazed_testing_data": partial_double_glazed_testing_data,
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"partial_secondary_glazed_testing_data": partial_secondary_glazed_testing_data,
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"pitched_roof_solar": pitched_roof_solar,
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"flat_roof_solar": flat_roof_solar
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}
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)
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)
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