Fixed broken tests

This commit is contained in:
Khalim Conn-Kowlessar 2023-10-18 15:52:55 +11:00
parent 08487d8f19
commit 8c5429c5d6
7 changed files with 32 additions and 61 deletions

2
.idea/Model.iml generated
View file

@ -7,7 +7,7 @@
<sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
</content>
<orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
<orderEntry type="jdk" jdkName="Python 3.10 (model_data)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
<component name="PyNamespacePackagesService">

2
.idea/misc.xml generated
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@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (model_data)" project-jdk-type="Python SDK" />
<component name="PythonCompatibilityInspectionAdvertiser">
<option name="version" value="3" />
</component>

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@ -86,7 +86,7 @@ def create_recommendation_scoring_data(
}
# Set staring u-values if we don't have them
if not scoring_dict["walls_thermal_transmittance"]:
if scoring_dict["walls_thermal_transmittance"] is None:
scoring_dict["walls_thermal_transmittance"] = get_wall_u_value(
clean_description=property.walls["clean_description"],
age_band=property.age_band,
@ -94,7 +94,7 @@ def create_recommendation_scoring_data(
is_sandstone_or_limestone=property.walls["is_sandstone_or_limestone"]
)
if not scoring_dict["floor_thermal_transmittance"]:
if scoring_dict["floor_thermal_transmittance"] is None:
scoring_dict["floor_thermal_transmittance"] = get_floor_u_value(
floor_type=property.floor_type,
area=property.floor_area,
@ -104,7 +104,7 @@ def create_recommendation_scoring_data(
age_band=property.age_band,
)
if not scoring_dict["roof_thermal_transmittance"]:
if scoring_dict["roof_thermal_transmittance"] is None:
scoring_dict["roof_thermal_transmittance"] = get_roof_u_value(
insulation_thickness=property.roof["insulation_thickness"],
has_dwelling_above=property.roof["has_dwelling_above"],
@ -130,7 +130,7 @@ def create_recommendation_scoring_data(
scoring_dict["walls_thermal_transmittance_ENDING"] = recommendation["new_u_value"]
scoring_dict["walls_insulation_thickness_ENDING"] = "above average"
else:
if not scoring_dict["walls_thermal_transmittance_ENDING"]:
if scoring_dict["walls_thermal_transmittance_ENDING"] is None:
scoring_dict["walls_thermal_transmittance_ENDING"] = get_wall_u_value(
clean_description=property.walls["clean_description"],
age_band=property.age_band,
@ -151,7 +151,7 @@ def create_recommendation_scoring_data(
# We don't really see above average for this in the training data
scoring_dict["floor_insulation_thickness_ENDING"] = "average"
else:
if not scoring_dict["floor_thermal_transmittance_ENDING"]:
if scoring_dict["floor_thermal_transmittance_ENDING"] is None:
scoring_dict["floor_thermal_transmittance_ENDING"] = get_floor_u_value(
floor_type=property.floor_type,
area=property.floor_area,
@ -167,7 +167,7 @@ def create_recommendation_scoring_data(
if recommendation["type"] not in ["wall_insulation", "floor_insulation"]:
raise NotImplementedError("Implement me")
if not scoring_dict["roof_thermal_transmittance_ENDING"]:
if scoring_dict["roof_thermal_transmittance_ENDING"] is None:
scoring_dict["roof_thermal_transmittance_ENDING"] = get_roof_u_value(
insulation_thickness=property.roof["insulation_thickness"],
has_dwelling_above=property.roof["has_dwelling_above"],
@ -180,7 +180,7 @@ def create_recommendation_scoring_data(
is_at_rafters=property.roof["is_at_rafters"],
)
if scoring_dict["roof_insulation_thickness_ENDING"] is None:
scoring_dict["roof_insulation_thickness_ENDING"] = "none"
if scoring_dict["roof_insulation_thickness_ENDING"] is None:
scoring_dict["roof_insulation_thickness_ENDING"] = "none"
return scoring_dict

View file

@ -1,15 +1,16 @@
from backend.Property import Property
from etl.epc.DataProcessor import DataProcessor
from backend.app.plan.utils import create_recommendation_scoring_data
from backend.app.plan.utils import create_recommendation_scoring_data, get_cleaned
from etl.epc.settings import COLUMNS_TO_MERGE_ON
from epc_api.client import EpcClient
import pandas as pd
import os
import pytest
import pickle
from utils.s3 import read_dataframe_from_s3_parquet
from tqdm import tqdm
# import pickle
# with open("sap_change_dataset.pickle", "rb") as f:
# sap_change_dataset = pickle.load(f)
#
@ -109,18 +110,14 @@ from tqdm import tqdm
class TestSapModelPrep:
@pytest.fixture
def cleaned(self):
with open(
os.path.abspath(os.path.dirname(__file__)) + "/test_data/cleaned.pickle", "rb"
) as f:
return pickle.load(f)
def cleaning_data(self):
return read_dataframe_from_s3_parquet(
bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
)
@pytest.fixture
def cleaning_data(self):
with open(
os.path.abspath(os.path.dirname(__file__)) + "/test_data/cleaning_data.pickle", "rb"
) as f:
return pickle.load(f)
def cleaned(self):
return get_cleaned()
def test_fill_cavity_wall(self, cleaned, cleaning_data):
"""
@ -395,7 +392,7 @@ class TestSapModelPrep:
'MULTI_GLAZE_PROPORTION_ENDING': 61.0, 'LOW_ENERGY_LIGHTING_ENDING': 17.0,
'NUMBER_OPEN_FIREPLACES_ENDING': 0.0, 'EXTENSION_COUNT_ENDING': 0.0, 'TOTAL_FLOOR_AREA_ENDING': 70.0,
'FLOOR_HEIGHT_ENDING': 3.64, 'DAYS_TO_STARTING': 2266, 'DAYS_TO_ENDING': 2307,
'walls_thermal_transmittance': 0.45, 'is_cavity_wall': False, 'is_filled_cavity': False,
'walls_thermal_transmittance': 1.7, 'is_cavity_wall': False, 'is_filled_cavity': False,
'is_solid_brick': True, 'is_system_built': False, 'is_timber_frame': False,
'is_granite_or_whinstone': False, 'is_as_built': True, 'is_cob': False,
'is_sandstone_or_limestone': False, 'is_park_home': False, 'walls_insulation_thickness': 'none',
@ -482,7 +479,7 @@ class TestSapModelPrep:
ending_lodgement_date2 = '2020-11-24'
starting_epc_data2["DAYS_TO_ENDING"] = data_processor2.calculate_days_to(ending_lodgement_date2)
ending_epc_data2["DAYS_TO_ENDING"] = data_processor2.calculate_days_to(ending_lodgement_date2)
recommendation2 = {
"recommendation_id": 0,

View file

@ -4,21 +4,21 @@ wall_uvalue_test_cases = [
"age_band": "A",
"is_granite_or_whinstone": False,
"is_sandstone_or_limestone": False,
"uvalue": 0.7
"uvalue": 1.3
},
{
"clean_description": "Cavity wall, as built, partial insulation",
"age_band": "F",
"is_granite_or_whinstone": False,
"is_sandstone_or_limestone": False,
"uvalue": 0.4
"uvalue": 0.85
},
{
"clean_description": "Cavity wall, as built, partial insulation",
"age_band": "F",
"age_band": "G",
"is_granite_or_whinstone": False,
"is_sandstone_or_limestone": False,
"uvalue": 0.4
"uvalue": 0.5375
},
{

View file

@ -118,6 +118,7 @@ class TestFloorRecommendations:
input_properties[2].age_band = "A"
input_properties[2].perimeter = 20
input_properties[2].wall_type = "solid brick"
input_properties[2].floor_type = "suspended"
recommender = FloorRecommendations(
property_instance=input_properties[2],
@ -160,6 +161,7 @@ class TestFloorRecommendations:
input_properties[4].age_band = "B"
input_properties[4].perimeter = 50
input_properties[4].wall_type = "solid brick"
input_properties[4].floor_type = "solid"
recommender = FloorRecommendations(
property_instance=input_properties[4],

View file

@ -407,36 +407,8 @@ class TestWallRecommendationsBase:
wall_recommendations_instance.property.data = {"property-type": "house"}
assert wall_recommendations_instance.ewi_valid is True
def test_recommend_without_u_value(self, wall_recommendations_instance):
wall_recommendations_instance.property.walls = {
"thermal_transmittance": None,
"is_solid_brick": False,
"is_cavity_wall": False,
"insulation_thickness": "none",
"clean_description": "Solid brick, as built, no insulation",
"is_granite_or_whinstone": False,
"is_sandstone_or_limestone": False,
}
wall_recommendations_instance.property.age_band = "A"
with pytest.raises(NotImplementedError):
wall_recommendations_instance.recommend()
class TestCavityWallRecommensations:
data = {
'low-energy-fixed-light-count': '', 'address': '123 Fake Street',
'floor-height': '', 'construction-age-band': 'England and Wales: 1950-1966',
'address3': '', 'property-type': 'House', 'local-authority-label': 'Melton',
'county': 'Leicestershire', 'postcode': 'LE14 2QH',
'solar-water-heating-flag': 'N', 'constituency': 'E14000909',
'number-heated-rooms': '5', 'local-authority': 'E07000133', 'built-form': 'End-Terrace',
'address1': '1, 23 fake', 'total-floor-area': '85.0', 'environment-impact-current': '49',
'number-habitable-rooms': 3, 'address2': 'Fake', 'posttown': 'IDK',
'walls-energy-eff': 'Poor', 'current-energy-rating': 'D',
'transaction-type': 'ECO assessment', 'uprn': '999', 'current-energy-efficiency': '57',
'lodgement-date': '2019-07-10', 'lmk-key': '999', 'tenure': 'rental (private)', 'floor-level': 'NODATA!',
'walls-description': 'Cavity wall, as built, no insulation (assumed)',
}
def test_fill_empty_cavity(self):
input_property = Property(id=1, postcode="F4k3", address1="123 fake street", epc_client=Mock())
@ -465,10 +437,10 @@ class TestCavityWallRecommensations:
assert recommender.recommendations
assert recommender.estimated_u_value == 1.5
assert np.isclose(recommender.recommendations[0]["new_u_value"], 0.25)
assert np.isclose(recommender.recommendations[0]["new_u_value"], 0.37)
assert np.isclose(recommender.recommendations[0]["cost"], 1000)
assert np.isclose(recommender.recommendations[1]["new_u_value"], 0.26)
assert np.isclose(recommender.recommendations[1]["new_u_value"], 0.38)
assert np.isclose(recommender.recommendations[1]["cost"], 1250)
def test_fill_partial_filled_cavity(self):
@ -498,10 +470,10 @@ class TestCavityWallRecommensations:
assert recommender.recommendations
assert recommender.estimated_u_value == 1.3
assert np.isclose(recommender.recommendations[0]["new_u_value"], 0.56)
assert np.isclose(recommender.recommendations[0]["new_u_value"], 0.43)
assert np.isclose(recommender.recommendations[0]["cost"], 1000)
assert np.isclose(recommender.recommendations[1]["new_u_value"], 0.57)
assert np.isclose(recommender.recommendations[1]["new_u_value"], 0.45)
assert np.isclose(recommender.recommendations[1]["cost"], 1250)
def test_system_built_wall(self):