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106 lines
4.2 KiB
Python
106 lines
4.2 KiB
Python
from typing import List, Dict, Any
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from collections import Counter
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import pandas as pd
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from model_data.epc_attributes.FloorAttributes import FloorAttributes
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from model_data.epc_attributes.HotWaterAttributes import HotWaterAttributes
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from model_data.epc_attributes.MainFuelAttributes import MainFuelAttributes
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from model_data.epc_attributes.MainheatAttributes import MainHeatAttributes
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from model_data.epc_attributes.MainheatControlAttributes import MainheatControlAttributes
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from model_data.epc_attributes.RoofAttributes import RoofAttributes
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from model_data.epc_attributes.WallAttributes import WallAttributes
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from model_data.epc_attributes.WindowAttributes import WindowAttributes
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from model_data.epc_attributes.LightingAttributes import LightingAttributes
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class EpcClean:
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"""
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Container for methods which we utilise for epc_attributes EPC data
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"""
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CLEANING_FIELDS: List[str] = [
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"floor-description",
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"hotwater-description",
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"main-fuel",
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"mainheat-description",
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"mainheatcont-description",
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"roof-description",
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"walls-description",
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"windows-description",
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"lighting-description"
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]
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def __init__(self, data: List[Dict[str, Any]]) -> None:
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"""
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EpcClean constructor.
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:param data: List of dictionaries containing EPC data.
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"""
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self.data: List[Dict[str, Any]] = data
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self.unique_vals: Dict[str, Any] = {}
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self.cleaned: Dict[str, List[Any]] = {}
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self.lighting_averages = self._calculate_lighting_averages()
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def _calculate_lighting_averages(self):
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"""
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This is a simple utility function that for few textual lighting descritpions, will calculate the average
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low energy lighting proportion. This is only valid for a very tiny number of cases and so a very simple
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methodology is applied
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:return: Dataframe of avergages for the corresponding descriptions
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"""
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df = pd.DataFrame(self.data)
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aggs = df[
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df["lighting-description"].isin(
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[
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'Below average lighting efficiency',
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'Good lighting efficiency',
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'Excelent lighting efficiency'
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]
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)
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].copy()
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aggs["low-energy-lighting"] = aggs["low-energy-lighting"].astype(float)
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averages = aggs.groupby("lighting-description")["low-energy-lighting"].mean().reset_index()
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averages["lighting-description"] = averages["lighting-description"].str.lower()
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return averages
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def clean(self) -> None:
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"""
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Cleans the EPC data, mapping text fields to property epc_attributes.
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"""
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self._init_empty_cleaned_obj()
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for field in self.CLEANING_FIELDS:
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self.unique_vals[field] = Counter([v[field] for v in self.data])
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self.clean_wrapper(field="floor-description", cleaning_cls=FloorAttributes)
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self.clean_wrapper(field="hotwater-description", cleaning_cls=HotWaterAttributes)
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self.clean_wrapper(field="main-fuel", cleaning_cls=MainFuelAttributes)
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self.clean_wrapper(field="mainheat-description", cleaning_cls=MainHeatAttributes)
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self.clean_wrapper(field="mainheatcont-description", cleaning_cls=MainheatControlAttributes)
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self.clean_wrapper(field="roof-description", cleaning_cls=RoofAttributes)
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self.clean_wrapper(field="walls-description", cleaning_cls=WallAttributes)
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self.clean_wrapper(field="windows-description", cleaning_cls=WindowAttributes)
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self.clean_wrapper(
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field="lighting-description", cleaning_cls=LightingAttributes, averages=self.lighting_averages
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)
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def _init_empty_cleaned_obj(self) -> None:
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"""
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Initializes an empty object for cleaned data.
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"""
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self.cleaned = {field: [] for field in self.CLEANING_FIELDS}
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def clean_wrapper(self, field, cleaning_cls, **kwargs):
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for description in self.unique_vals[field].keys():
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self.cleaned[field].append(
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{
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"original_description": description,
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**cleaning_cls(description, **kwargs).process()
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}
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)
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