Added anomaly handling into classes by introducing the BaseUtility class

This commit is contained in:
Khalim Conn-Kowlessar 2023-06-20 10:20:30 +01:00
parent 2c17b983d0
commit 9e61f3c8fb
4 changed files with 78 additions and 18 deletions

54
model_data/BaseUtility.py Normal file
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@ -0,0 +1,54 @@
class BaseUtility:
"""
This class contains some base attributes which are used across multiple other classes
"""
# Anomalies described here: https://epc.opendatacommunities.org/docs/guidance#glossary
DATA_ANOMALY_MATCHES = {
# Invalid reports are where the value provided is out of bounds, e.g. a negative energy rating of -1199 or a
# non-integer, there is no valid energy band for this, so it is marked as INVALID!
"INVALID",
# When the energy certificate was first lodged on the register there was no requirement to lodge this data
# item, i.e. a non-mandatory item.
"NO DATA!",
# When the energy certificate was first lodged on the register there was no requirement to lodge this data item,
# i.e.a non - mandatory item.
"N/A",
# A value generated by the register to account for a data item that was not mandatory when the lodgement of
# the energy certificate occurred. When the data item became mandatory the register operator, for backwards
# compatibility purposes, populated the data field with a value of not recorded to ensure that the energy
# certificate retrieval process is successfully completed. Mandatory data items cannot be applied
# retrospectively to energy certificates lodged before the date of the change.
"Not recorded",
# The data also contains DECs with an operational rating of 9999 (a default DEC). The production of a
# default DEC value was allowed to enable building occupiers, with poor quality or no energy data,
# the opportunity to comply with the regulations. From April 2011 the ability to lodge a default DEC was no
# longer allowed.
"9999",
# The Building Emission Rate (BER) data field for non-domestic buildings may contain a blank value. The BER
# was only lodged on the register from 7 March 2010.
"Blank"
# There are currently just over 8,600 records where the local authority identifier is null. This is due to
# the Register Operator not being able to match the building address in the Markermap Ordinance Survey (GB)
# lookup tables or OS MasterMap Address Layer 2 data. The majority of these addresses have been requested
# manually by energy assessors for inclusion by the Register Operator in the registers (e.g. new builds,
# etc). These records are being published for completeness. An ongoing process to manage these manually added
# addresses will take time to develop to deal with these and future anomalies.
#
# There are several fields within the lodged data where it is possible to enter multiple entries to cater for
# different types of build within a single property, i.e. extensions. This results in multiple entries for
# the description fields for floor, roof and wall. For the purposes of this data release only the information
# contained within the first of these multiple entries is being provided. As there are no restrictions on the
# value in this first field it means that sometimes the first field in a multiple entry description field may
# contain a null value. A resolution to correct these anomalies will be considered for future data releases.
"NULL"
}
DATA_ANOMALY_SUBSTRINGS = {
# Where values in a pick list that have been superseded by another value. For example, where a value for
# pitched roof has been replaced by three sub-categories of pitched roof. The original value is retained
# but for backward compatibility only it is appended to ensure that the energy certificate retrieval
# process can be successfully completed. Replacement data items cannot be applied retrospectively to energy
# certificates lodged on the register before the date of the change.
"for backward compatibility only"
}

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@ -2,9 +2,10 @@ from epc_api.client import EpcClient
from model_data.config import EPC_AUTH_TOKEN
from model_data.OpenUprnClient import OpenUprnClient
from model_data.EpcClean import EpcClean
from model_data.BaseUtility import BaseUtility
class Property:
class Property(BaseUtility):
ATTRIBUTE_MAP = {
"floor-description": "floor",
"hotwater-description": "hotwater",
@ -91,6 +92,10 @@ class Property:
for description, attribute in cleaner.cleaned.items():
if self.data[description] in self.DATA_ANOMALY_MATCHES:
setattr(self, self.ATTRIBUTE_MAP[description], {"original_description": self.data[description]})
continue
attributes = [
x for x in cleaner.cleaned[description] if x["original_description"] == self.data[description]
]

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@ -1,5 +1,3 @@
import pandas as pd
from pprint import pprint
from tqdm import tqdm
import os
from model_data.BoreholeClient import BoreholeClient
@ -54,8 +52,6 @@ def handler():
for p in input_properties:
p.get_coordinates(open_uprn_client)
pprint(input_properties[0].coordinates)
local_authorities = {p.data['local-authority'] for p in input_properties}
data = []
@ -69,19 +65,10 @@ def handler():
)
)
cleaner = EpcClean(data)
# Incorporate input data into cleaning
cleaner = EpcClean(data + [p.data for p in input_properties])
cleaner.clean()
# example cleaned data
# Why do we need this stuff?
# https://docs.google.com/spreadsheets/d/1ek9ItDv7xHwFm_FK6B0PyOBwvi6U4qRPuncBsVlCHUA/edit#gid=0
cleaner.cleaned.keys()
floors = pd.DataFrame(cleaner.cleaned['floor-description'])
walls = pd.DataFrame(cleaner.cleaned['walls-description'])
hotwater = pd.DataFrame(cleaner.cleaned['hotwater-description'])
mainheat = pd.DataFrame(cleaner.cleaned["mainheat-description"])
address_meta = [
{
"postcode": x["postcode"].upper(),
@ -110,3 +97,5 @@ def handler():
# on the cleaning we've done
for p in input_properties:
p.get_components(cleaner)
# Now, given the components, we want to idenfity upgrade options

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@ -1,8 +1,9 @@
from typing import Dict, Union
from model_data.BaseUtility import BaseUtility
from model_data.epc_attributes.attribute_utils import clean_description, remove_punctuation, find_keyword
class MainFuelAttributes:
class MainFuelAttributes(BaseUtility):
FUEL_KEYWORDS = [
'heat network',
'mains gas',
@ -42,7 +43,7 @@ class MainFuelAttributes:
self.is_community = 'community' in self.description and 'not community' not in self.description
self.is_unknown = False
self.nodata = not description
self.nodata = not description or description in self.DATA_ANOMALY_MATCHES
if not self.nodata and not any(
self._keyword_in_description(keywords)
@ -59,6 +60,17 @@ class MainFuelAttributes:
return any(keyword in self.description for keyword in keywords)
def process(self) -> Dict[str, Union[str, bool]]:
if self.nodata:
result = {
"fuel_type": None,
"tariff_type": None,
"is_community": False,
"no_individual_heating_or_community_network": False,
"complex_fuel_type": False
}
return result
result: Dict[str, Union[str, bool]] = {
"fuel_type": find_keyword(self.description, self.FUEL_KEYWORDS),
"tariff_type": find_keyword(self.description, self.TARIFF_KEYWORDS),