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refactoring recommendations
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Khalim Conn-Kowlessar
parent
deda5bc025
commit
2a002c1faf
6 changed files with 92 additions and 60 deletions
2
.idea/Model.iml
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2
.idea/Model.iml
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@ -6,7 +6,7 @@
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<sourceFolder url="file://$MODULE_DIR$/model_data" isTestSource="false" />
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<sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
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</content>
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<orderEntry type="jdk" jdkName="Python 3.10 (hestia-data)" jdkType="Python SDK" />
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<orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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2
.idea/misc.xml
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2
.idea/misc.xml
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@ -1,6 +1,6 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (hestia-data)" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
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<component name="PythonCompatibilityInspectionAdvertiser">
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<option name="version" value="3" />
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</component>
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@ -102,18 +102,52 @@ async def trigger_plan(body: PlanTriggerRequest):
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logger.info("Getting components and properties recommendations")
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for p in input_properties:
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recommendations = []
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for property_id, p in enumerate(input_properties):
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# For each property, classiy floor area decide
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total_floor_area_group_decile = classify_decile_newvalues(
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decile_boundaries=floors_decile_data["decile_boundaries"],
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decile_labels=floors_decile_data["decile_labels"],
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new_values=[float(p.data["total-floor-area"])],
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)[0]
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print("hey")
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print(total_floor_area_group_decile)
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for p in input_properties:
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# Property recommendations
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p.get_components(cleaned)
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# floor_recommendations = FloorRecommendations(property_instance=p, uvalue_estimates=uvalue_estimates_floors)
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# Floor recommendations
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floor_recommender = FloorRecommendations(property_instance=p, uvalue_estimates=uvalue_estimates_floors)
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floor_recommender.recommend()
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floor_recommendations = floor_recommender.recommendations
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# insert property id
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for rec in floor_recommendations:
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rec["property_id"] = property_id
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# Wall recommendations
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# We would make this u-value query directly to the database
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total_floor_area_group_decile = classify_decile_newvalues(
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decile_boundaries=walls_decile_data["decile_boundaries"],
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decile_labels=walls_decile_data["decile_labels"],
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new_values=[float(p.data["total-floor-area"])],
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)[0]
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# This
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walls_u_value_estimate = [
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x for x in uvalue_estimates_walls
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if (x['local-authority'] == p.data["local-authority"]) &
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(x['property-type'] == p.data["property-type"]) &
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(x['built-form'] == p.data["built-form"]) &
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(x['walls-energy-eff'] == p.data["walls-energy-eff"]) &
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(x['walls-env-eff'] == p.data["walls-env-eff"]) &
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(x['total-floor-area_group'] == total_floor_area_group_decile)
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]
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wall_recomendations = WallRecommendations(property_instance=p, uvalue_estimates=walls_u_value_estimate)
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wall_recomendations.recommend()
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wall_recomendations = wall_recomendations.recommendations
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# insert property id
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for rec in wall_recomendations:
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rec["property_id"] = property_id
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recommendations.extend(floor_recommendations)
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return {"message": "Plan complete"}
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@ -341,20 +341,20 @@ def app():
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)
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# Production of sample data for land registry
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address_meta = [
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{
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"postcode": x["postcode"].upper(),
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"address1": x["address1"].upper(),
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"address2": x["address2"].upper(),
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"address3": x["address3"].upper(),
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"address": x["address"],
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"uprn": x["uprn"]
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} for x in data
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]
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import pickle
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with open("sample_addresses.pkl", "wb") as f:
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pickle.dump(address_meta, f)
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# address_meta = [
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# {
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# "postcode": x["postcode"].upper(),
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# "address1": x["address1"].upper(),
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# "address2": x["address2"].upper(),
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# "address3": x["address3"].upper(),
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# "address": x["address"],
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# "uprn": x["uprn"]
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# } for x in data
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# ]
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#
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# import pickle
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# with open("sample_addresses.pkl", "wb") as f:
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# pickle.dump(address_meta, f)
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# Incorporate input data into cleaning
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cleaner = EpcClean(data)
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@ -88,7 +88,9 @@ class FloorRecommendations(BaseUtility):
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"Ground": 0,
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# We don't know what floor level, we just make sure it's not 0
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"mid floor": 1,
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"4th": 4
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"4th": 4,
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# We set
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"00": 0,
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}
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def __init__(self, property_instance: Property, uvalue_estimates):
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@ -253,7 +255,7 @@ class FloorRecommendations(BaseUtility):
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self.recommend_floor_insulation(u_value=u_value, parts=solid_floor_insulation_parts)
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def _get_floors_uvalue_estimate(self, total_floor_area_group_decile):
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blah
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"""
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Wrapper function which contains the methodology to extract a property's walls u-value estimate
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when we don't have a true value and if we can't base our assumption off of the material
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@ -1,5 +1,6 @@
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import itertools
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import math
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from statistics import mean
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from model_data.Property import Property
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from model_data.BaseUtility import BaseUtility
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@ -321,10 +322,12 @@ class WallRecommendations(BaseUtility):
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if new_u_value <= self.BUILDING_REGULATIONS_PART_L_MAX_U_VALUE:
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lowest_selected_u_value = update_lowest_selected_u_value(lowest_selected_u_value, new_u_value)
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recommendations.append({
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"parts": [get_recommended_part(part, depth)],
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"new_u_value": new_u_value,
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})
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recommendations.append(
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{
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"parts": [get_recommended_part(part, depth)],
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"new_u_value": new_u_value,
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}
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)
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return recommendations
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@ -405,46 +408,39 @@ class WallRecommendations(BaseUtility):
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:return:
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"""
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total_floor_area_group_decile = self.uvalue_estimates.classify_decile_newvalues(
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decile_boundaries=self.uvalue_estimates.walls_decile_data["decile_boundaries"],
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decile_labels=self.uvalue_estimates.walls_decile_data["decile_labels"],
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new_values=[float(self.property.data["total-floor-area"])],
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)[0]
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u_value_estimate = self.uvalue_estimates.walls[
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(self.uvalue_estimates.walls["local-authority"] == self.property.data["local-authority"]) &
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(self.uvalue_estimates.walls["property-type"] == self.property.data["property-type"]) &
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(self.uvalue_estimates.walls["built-form"] == self.property.data["built-form"]) &
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(self.uvalue_estimates.walls["walls-energy-eff"] == self.property.data["walls-energy-eff"]) &
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(self.uvalue_estimates.walls["walls-env-eff"] == self.property.data["walls-env-eff"]) &
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(self.uvalue_estimates.walls["total-floor-area_group"] == total_floor_area_group_decile)
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]
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if u_value_estimate.empty:
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if not self.uvalue_estimates:
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raise ValueError("No U-value estimate found for the given property")
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# Because of how spuriously populated the data is for number-habitable-rooms and number-heated-rooms,
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# we will try and filter on these to see if we get a result
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habitable_rooms_filter = (
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self.uvalue_estimates.walls["number-habitable-rooms"] == self.property.data["number-habitable-rooms"]
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habitable_rooms_filer = [
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x for x in self.uvalue_estimates if
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x["number-habitable-rooms"] == self.property.data["number-habitable-rooms"]
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]
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if not habitable_rooms_filer:
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# Take a mean of all the u-value estimates
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return mean(
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[x["median_thermal_transmittance"] for x in self.uvalue_estimates if x["median_thermal_transmittance"]]
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)
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# Try perform a filter on heated rooms
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heated_rooms_filter = [
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x for x in habitable_rooms_filer if
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x["number-heated-rooms"] == self.property.data["number-heated-rooms"]
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]
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if not heated_rooms_filter:
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# Take a mean of all the u-value estimates
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return mean(
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[x["median_thermal_transmittance"] for x in habitable_rooms_filer if x["median_thermal_transmittance"]]
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)
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return mean(
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[x["median_thermal_transmittance"] for x in heated_rooms_filter if x["median_thermal_transmittance"]]
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)
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if any(habitable_rooms_filter):
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u_value_estimate = u_value_estimate[habitable_rooms_filter]
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heated_rooms_filter = (
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self.uvalue_estimates.walls["number-heated-rooms"] == self.property.data["number-heated-rooms"]
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)
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if any(heated_rooms_filter):
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u_value_estimate = u_value_estimate[heated_rooms_filter]
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# It's possible for us to have multiple rows if we didn't do a habitable/heated rooms filter so we
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# average
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return u_value_estimate["median_thermal_transmittance"].mean()
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@staticmethod
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def rvalue_per_mm(total_r_value: float, thickness_mm: float) -> float:
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"""Return R-value per mm.
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