Model/recommendations/Recommendations.py

import pandas as pd
import numpy as np
from backend.Property import Property
from typing import List
from itertools import groupby
from recommendations.FloorRecommendations import FloorRecommendations
from recommendations.WallRecommendations import WallRecommendations
from recommendations.RoofRecommendations import RoofRecommendations
from recommendations.VentilationRecommendations import VentilationRecommendations
from recommendations.FireplaceRecommendations import FireplaceRecommendations
from recommendations.LightingRecommendations import LightingRecommendations
from recommendations.SolarPvRecommendations import SolarPvRecommendations
from recommendations.WindowsRecommendations import WindowsRecommendations
from recommendations.HeatingRecommender import HeatingRecommender
from recommendations.HotwaterRecommendations import HotwaterRecommendations
from recommendations.SecondaryHeating import SecondaryHeating
from recommendations.DraughtProofingRecommendations import DraughtProofingRecommendations
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
from backend.apis.GoogleSolarApi import GoogleSolarApi
import backend.app.assumptions as assumptions
from backend.app.plan.schemas import SPECIFIC_MEASURES, MEASURE_MAP, NON_INVASIVE_SPECIFIC_MEASURES

STARTING_DUMMY_ID_VALUE = -9999


class Recommendations:
    """
    High level recommendations class, which sits above the measure specific recommendation classes
    """

    def __init__(
        self,
        property_instance: Property,
        materials: List,
        exclusions: List[str] = None,
        inclusions: List[str] = None,
        default_u_values: bool = False,
    ):
        """
        :param property_instance: Instance of the Property class, for the home associated to property_id
        :param materials: List of materials to be used in the recommendations
        :param exclusions: List of specific measures or measure types to exclude from recommendations. Defaulted to
                            None, meaning no exclusions to be applied
        :param inclusions: List of specific measures of measure types to include. Defaulted to None, meaning all
                           measures are included
        :param default_u_values: Boolean, if True, the recommendations will use the default u-values for the property
        """

        self.property_instance = property_instance
        self.materials = materials
        self.exclusions = exclusions if exclusions else []
        self.inclusions = inclusions if inclusions else []
        self.default_u_values = default_u_values

        self.all_specific_measures = SPECIFIC_MEASURES
        self.all_non_invase_measures = NON_INVASIVE_SPECIFIC_MEASURES
        self.non_invasive_recommendation_types = [
            r["type"] for r in self.property_instance.non_invasive_recommendations
        ]

        self.floor_recommender = FloorRecommendations(property_instance=property_instance, materials=materials)
        self.wall_recomender = WallRecommendations(property_instance=property_instance, materials=materials)
        self.roof_recommender = RoofRecommendations(property_instance=property_instance, materials=materials)
        self.ventilation_recomender = VentilationRecommendations(
            property_instance=property_instance, materials=materials
        )
        self.draught_proofing_recommender = DraughtProofingRecommendations(property_instance=property_instance)
        self.fireplace_recommender = FireplaceRecommendations(property_instance=property_instance)
        self.lighting_recommender = LightingRecommendations(property_instance=property_instance, materials=materials)
        self.windows_recommender = WindowsRecommendations(property_instance=property_instance, materials=materials)
        self.solar_recommender = SolarPvRecommendations(property_instance=property_instance)
        self.heating_recommender = HeatingRecommender(property_instance=property_instance)
        self.hotwater_recommender = HotwaterRecommendations(property_instance=property_instance)
        self.secondary_heating_recommender = SecondaryHeating(property_instance=property_instance)

    def find_included_measures(self):
        """
        Determines the set of measures to be included in recommendations
        """

        # Generally, inclusions is a global option and will overrule specific property non-invasive recommendations.
        # This is done so that we can use inclusions to specify scenarios.

        inclusions_full = [MEASURE_MAP[x] if x in MEASURE_MAP else x for x in self.inclusions]
        exclusions_full = [MEASURE_MAP[x] if x in MEASURE_MAP else x for x in self.exclusions]
        # We need to unlist any lists, but we should check if they're lists first
        inclusions_full = [
            item for sublist in inclusions_full for item in (sublist if isinstance(sublist, list) else [sublist])
        ]
        exclusions_full = [
            item for sublist in exclusions_full for item in (sublist if isinstance(sublist, list) else [sublist])
        ]

        # If inclusions and exclusions are empty, it means that nothing was specified, so we allow
        # all recommendation types
        if not inclusions_full and not exclusions_full:
            # All typical measures - this does not include non-invasive measures inless they are specified
            return self.all_specific_measures + self.non_invasive_recommendation_types

        if inclusions_full:
            return inclusions_full

        if exclusions_full:
            measures = [
                m for m in self.all_specific_measures + self.non_invasive_recommendation_types
                if m not in exclusions_full
            ]
            return measures

    def recommend(self):

        """
        This method runs the recommendations for the individual measures and then appends them to a list for output

        The recommendations are implemented in order of suggested phase, from fabric first to heating systems, to
        renewables.
        :return:
        """

        property_recommendations = []
        phase = 0
        measures = self.find_included_measures()
        non_invasive_recommendation_types = [r["type"] for r in self.property_instance.non_invasive_recommendations]

        # Building Fabric
        self.wall_recomender.recommend(phase=phase, measures=measures, default_u_values=self.default_u_values)
        if self.wall_recomender.recommendations:
            property_recommendations.append(self.wall_recomender.recommendations)
            phase += 1

        # We handle recommendations covering specific non-invasive measures
        new_phase = self.wall_recomender.recommend_extended(phase=phase, measures=measures)
        if self.wall_recomender.extended_recommendations:
            property_recommendations.append(self.wall_recomender.extended_recommendations)
            # We don't have any phasing here
            phase = new_phase

        self.roof_recommender.recommend(phase=phase, measures=measures, default_u_values=self.default_u_values)
        if self.roof_recommender.recommendations:
            property_recommendations.append(self.roof_recommender.recommendations)
            phase += 1

        # Ventilation recommendations
        # We only produce a ventilation recommendation if the property is recommended to have wall or roof
        # insulation
        # We will not attribute a SAP impact to the ventilation recommendation, since we've seen that this
        # has no
        # real impact on the SAP score. Therefore, we don't need to include phasing for ventilation. If we
        # have any
        # wall or roof recommendations, we will ensure that ventilation is included in the simulation
        if (
            (self.wall_recomender.recommendations or self.roof_recommender.recommendations) and
            ("ventilation" in measures)
        ):
            self.ventilation_recomender.recommend()
            if self.ventilation_recomender.recommendation:
                property_recommendations.append(self.ventilation_recomender.recommendation)

        if "trickle_vents" in measures:
            # This is a recommendatin that typically comes from an energy assessment
            trickle_vents_rec = self.ventilation_recomender.recommend_trickle_vents()
            if trickle_vents_rec:
                property_recommendations.append(trickle_vents_rec)

        if "draught_proofing" in measures:
            # This is a recommendation that in some instances we can recommend, by deducing it from the SAP
            # recommendations, however we will implement this later
            self.draught_proofing_recommender.recommend()
            if self.draught_proofing_recommender.recommendation:
                property_recommendations.append(self.draught_proofing_recommender.recommendation)

        self.floor_recommender.recommend(phase=phase, measures=measures)
        if self.floor_recommender.recommendations:
            property_recommendations.append(self.floor_recommender.recommendations)
            phase += 1

        if "low_energy_lighting" in measures:
            self.lighting_recommender.recommend(phase=phase)
            if self.lighting_recommender.recommendation:
                property_recommendations.append(self.lighting_recommender.recommendation)
                phase += 1

        if "mixed_glazing" not in non_invasive_recommendation_types:
            # If we have a mixed glazing recommendation, we prioritise this over the windows recommendation
            self.windows_recommender.recommend(phase=phase, measures=measures)
            if self.windows_recommender.recommendation:
                property_recommendations.append(self.windows_recommender.recommendation)
                phase += 1

        if "mixed_glazing" in measures:
            # This is a recommendation that comes exclusively from an energy assessment
            mixed_glazing_rec = self.windows_recommender.recommend_mixed_glazing(phase=phase)
            if mixed_glazing_rec:
                property_recommendations.append(mixed_glazing_rec)
                phase += 1

        if "fireplace" in measures:
            self.fireplace_recommender.recommend(phase=phase)
            if self.fireplace_recommender.recommendation:
                property_recommendations.append(self.fireplace_recommender.recommendation)
                phase += 1

        cavity_or_loft_recommendations = [
            r for r in self.wall_recomender.recommendations + self.roof_recommender.recommendations
            if r["type"] in ["cavity_wall_insulation", "loft_insulation"]
        ]
        has_cavity_or_loft_recommendations = len(cavity_or_loft_recommendations) > 0

        self.heating_recommender.recommend(
            phase=phase,
            measures=measures,
            has_cavity_or_loft_recommendations=has_cavity_or_loft_recommendations,
        )
        if (
            self.heating_recommender.heating_recommendations or
            self.heating_recommender.heating_control_recommendations
        ):

            # We split into first and second phase recommendations
            first_phase_recommendations = [
                r for r in (
                    self.heating_recommender.heating_recommendations +
                    self.heating_recommender.heating_control_recommendations
                )
                if r["phase"] == phase
            ]
            second_phase_recommendations = [
                r for r in (
                    self.heating_recommender.heating_recommendations +
                    self.heating_recommender.heating_control_recommendations
                )
                if r["phase"] == phase + 1
            ]

            if first_phase_recommendations:
                property_recommendations.append(first_phase_recommendations)

            if second_phase_recommendations:
                property_recommendations.append(second_phase_recommendations)

            # We check if we have distinct heating and heating controls recommendations
            # If so, we increment by 2 (one of the heating system, one for the heating controls)
            # otherwise we incremenet by 1
            max_used_phase = max(
                [rec["phase"] for rec in
                 self.heating_recommender.heating_recommendations +
                 self.heating_recommender.heating_control_recommendations]
            )
            amount_to_increment = max_used_phase - phase + 1
            phase += amount_to_increment

        # Hot water
        if "hot_water" in measures:
            self.hotwater_recommender.recommend(phase=phase)
            if self.hotwater_recommender.recommendations:
                property_recommendations.append(self.hotwater_recommender.recommendations)
                phase += 1

        if "secondary_heating" in measures:
            self.secondary_heating_recommender.recommend(phase=phase)
            if self.secondary_heating_recommender.recommendation:
                property_recommendations.append(self.secondary_heating_recommender.recommendation)
                phase += 1

        # Renewables
        if "solar_pv" in measures:
            self.solar_recommender.recommend(phase=phase)
            if self.solar_recommender.recommendation:
                property_recommendations.append(self.solar_recommender.recommendation)
                phase += 1

        # We insert temporary ids into the recommendations which is important for the optimiser later
        property_recommendations = self.insert_temp_recommendation_id(property_recommendations)

        # We also need to create the representative recommendations for each recommendation type
        property_representative_recommendations = self.create_representative_recommendations(
            property_recommendations,
        )

        # Check to make sure measure_type is populated
        for recs in property_recommendations:
            if any(pd.isnull(rec.get("measure_type")) for rec in recs):
                raise ValueError("Measure type is not populated")

        return property_recommendations, property_representative_recommendations

    @staticmethod
    def create_representative_recommendations(property_recommendations):
        """
        This method will create a representative recommendation for each recommendation type
        In order to create a representative recommendation, we choose the recommendation that has:
        1) Where a U-value is available, has the best U-value to cost ratio
        2) Where SAP points are available, has the best SAP points to cost ratio

        We don't include mechanical ventilation in the representative recommendations, since we don't attribute a
        SAP impact to this recommendation
        :return:
        """
        property_representative_recommendations = []

        for recommendations_by_type in property_recommendations:

            # If the property was initially surveyed as filled, but the cavity was only partially filled, we don't
            # want to include the cavity wall insulation recommendation in the defaults

            if recommendations_by_type[0].get("type") in [
                "mechanical_ventilation", "trickle_vents", "draught_proofing"
            ]:
                continue

            has_u_value = recommendations_by_type[0].get("new_u_value") is not None
            has_sap_points = recommendations_by_type[0].get("sap_points") is not None
            has_rank = recommendations_by_type[0].get("rank") is not None

            # When check if these recommendations have two different types, such as solid wall insulation
            # If we have multiple types, we group by type and then select the best recommendation for each type

            # If we have a heating and heating control recommendation, we use JUST the heating reommendation
            has_both_heating_types = all(
                x in [rec["type"] for rec in recommendations_by_type] for x in ["heating", "heating_control"]
            )
            if has_both_heating_types:
                # Take just heating
                recommendations_by_type = [
                    rec for rec in recommendations_by_type if rec["type"] == "heating"
                ]

            recommendations_by_type = sorted(recommendations_by_type, key=lambda x: x["type"])
            representative_recommendations = []
            for _type, recommendations in groupby(recommendations_by_type, key=lambda x: x["type"]):
                recommendations = list(recommendations)
                # We also create an efficiency key, which is used to sort the recommendations
                if has_u_value:
                    # We sort by the cost per U-value improvement - the lower the better
                    for rec in recommendations:
                        rec["efficiency"] = rec["total"] / rec["starting_u_value"] - rec["new_u_value"]
                elif not has_u_value and has_sap_points:
                    # Sort the options by the cost per SAP point improvement - the lower the better
                    for rec in recommendations:
                        if rec["sap_points"] == 0:
                            rec["efficiency"] = 0
                        else:
                            rec["efficiency"] = rec["total"] / rec["sap_points"]
                elif has_rank:
                    # Sort the options by rank - the lower the better
                    for rec in recommendations:
                        rec["efficiency"] = rec["rank"]
                else:
                    # Sort the options by cost - the lower the better
                    for rec in recommendations:
                        rec["efficiency"] = rec["total"]

                recommendations.sort(
                    key=lambda x: x["efficiency"]
                )
                representative_recommendations.append(recommendations[0])
            property_representative_recommendations.extend(representative_recommendations)

        return property_representative_recommendations

    @staticmethod
    def insert_temp_recommendation_id(property_recommendations):
        """
        Creates a temporary recommendation id which is needed for
        filtering recommendations between default and no, after the optimiser has been
        run
        :param property_recommendations:  nested list of recommendations, grouped by data_types
        :return: Updated recommendations_to_upload, where where recommendation has a "recommendation_id"
                 integer inserted
        """
        idx = 0

        for recs in property_recommendations:
            for rec in recs:
                rec["recommendation_id"] = f"{str(idx)}_phase={str(rec['phase'])}"
                idx += 1

        return property_recommendations

    @staticmethod
    def _calculate_appliance_solar_savings(
        rec, property_instance, heating_kwh_reduction, hot_water_kwh_reduction, lighting_kwh_reduction
    ):
        """
        Calculates the impact on kwh and cost of installing solar panels on appliances
        :param rec: The recommendation
        :param property_instance: Instance of the Property class
        :param heating_kwh_reduction: The kwh reduction from heating
        :param hot_water_kwh_reduction: The kwh reduction from hot water
        :param lighting_kwh_reduction: The kwh reduction from lighting
        :return:
        """

        if rec["type"] != "solar_pv":
            return 0, 0

        if property_instance.solar_panel_configuration is None:
            print("PLACEHOLDER ESTIMATES")
            # 50% reduction average
            kwh_reduction = property_instance.energy_consumption_estimates["adjusted"]["appliances"] * 0.5
            predicted_appliances_cost_reduction = kwh_reduction * AnnualBillSavings.ELECTRICITY_PRICE_CAP
            return predicted_appliances_cost_reduction, kwh_reduction

        # Calulate the amount of energy the solar panel array will generate for this unit
        unit_energy_consumption = (
            rec["initial_ac_kwh_per_year"] *
            property_instance.solar_panel_configuration["unit_share_of_energy"]
        )

        unit_energy_utilised = unit_energy_consumption * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION
        unit_energy_exported = unit_energy_consumption - unit_energy_utilised
        unit_energy_exported_value = unit_energy_exported * AnnualBillSavings.ELECTRICITY_EXPORT_PAYMENT

        # We assume that 50% of the energy generated will be used by the property without a battery
        # to be conservative

        # of the energy utilised, some of it is used by heating, hot water and lighting so we
        # remove that from the total
        unit_energy_utilised -= (
            heating_kwh_reduction + hot_water_kwh_reduction + lighting_kwh_reduction
        )
        unit_energy_utilised = 0 if unit_energy_utilised < 0 else unit_energy_utilised

        # This is how much energy the appliances will use after install
        post_install_appliance_kwh = (
            property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
            unit_energy_utilised
        )
        post_install_appliance_kwh = (
            0 if post_install_appliance_kwh < 0 else post_install_appliance_kwh
        )

        predicted_appliances_kwh_reduction = (
            property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
            post_install_appliance_kwh
        )

        predicted_appliances_cost_reduction = unit_energy_exported_value + (
            predicted_appliances_kwh_reduction * AnnualBillSavings.ELECTRICITY_PRICE_CAP
        )

        return predicted_appliances_cost_reduction, predicted_appliances_kwh_reduction

    @classmethod
    def calculate_recommendation_impact(
        cls,
        property_instance,
        all_predictions,
        recommendations,
    ):

        """
        Given predictions from the model apis, with method will update the recommendations with the predicted
        impact of the recommendation on the property

        This function will return two objects:
        1) Updated recommendations with the predicted impact of the recommendation
        2) A list of impacts by phase, which will be used for the kwh model scoring

        :param property_instance: Instance of the Property class, for the home associated to property_id
        :param all_predictions: dictionary of predictions from the model apis
        :param recommendations: dictionary of recommendations for the property
        :return:
        """

        property_predictions = {
            prefix + "_predictions": all_predictions[prefix + "_predictions"][
                all_predictions[prefix + "_predictions"]["property_id"] == str(property_instance.id)
                ].copy() for prefix in ["sap_change", "heat_demand", "carbon_change"]
        }

        property_recommendations = recommendations[property_instance.id].copy()

        increasing_variables = ["sap"]
        decreasing_variables = ["carbon", "heat_demand"]

        impact_summary = []
        for recommendations_by_type in property_recommendations:
            for rec in recommendations_by_type:
                if rec["type"] in [
                    "mechanical_ventilation", "trickle_vents", "draught_proofing", "extension_cavity_wall_insulation"
                ]:
                    # We don't have a percieved sap impact of mechanical ventilation or trickle vents, and we don't
                    # have the capacity to score draught proofing
                    if rec["type"] == "extension_cavity_wall_insulation":

                        previous_phase = [x for x in impact_summary if x["phase"] == (rec["phase"] - 1)]
                        if previous_phase:
                            sap = previous_phase[0]["sap"]
                            carbon = previous_phase[0]["carbon"]
                            heat_demand = previous_phase[0]["heat_demand"]
                        else:
                            sap = float(property_instance.data["current-energy-efficiency"])
                            carbon = float(property_instance.data["co2-emissions-current"])
                            heat_demand = float(property_instance.data["energy-consumption-current"])

                        impact_summary.append(
                            {
                                "phase": rec["phase"],
                                "recommendation_id": rec["recommendation_id"],
                                "sap": sap + rec["sap_points"],
                                "carbon": carbon - rec["co2_equivalent_savings"],
                                "heat_demand": heat_demand - rec["heat_demand"],
                            }
                        )
                    continue

                phase_energy_efficiency_metrics = {
                    prefix: property_predictions[prefix + "_predictions"][
                        property_predictions[prefix + "_predictions"]["recommendation_id"] == str(
                            rec["recommendation_id"]
                        )]["predictions"].values[0] for prefix in ["sap_change", "heat_demand", "carbon_change"]
                }

                # We structure this so that depending on the phase, we capture the previous phase impacts and
                # then just have one piece of code to calculate the difference
                if rec["phase"] == 0:
                    # These are just the starting values, from the EPC. When we score the ML models,
                    # heating_cost_starting and heating_cost_ending are just the values in the EPC. However, with
                    # heating_cost_ending, we expect that the EPC will predict a heating cost based on what would happen
                    # if we implemented the recommendation today, so our starting value is the EPC
                    previous_phase_values = {
                        "sap": float(property_instance.data["current-energy-efficiency"]),
                        "carbon": float(property_instance.data["co2-emissions-current"]),
                        "heat_demand": float(property_instance.data["energy-consumption-current"]),
                    }

                else:

                    previous_phase_values_multiple = [x for x in impact_summary if x["phase"] == (rec["phase"] - 1)]
                    if len(previous_phase_values_multiple) != 1:
                        # Take an average of each of the previous phases
                        keys_to_median = ["sap", "carbon", "heat_demand"]

                        previous_phase_values = {}
                        for key in keys_to_median:
                            values = [item[key] for item in previous_phase_values_multiple]
                            previous_phase_values[key] = np.median(values)

                    else:
                        previous_phase_values = previous_phase_values_multiple[0]

                # We extract the values for the current phase
                current_phase_values = {
                    "sap": phase_energy_efficiency_metrics["sap_change"],
                    "carbon": phase_energy_efficiency_metrics["carbon_change"],
                    "heat_demand": phase_energy_efficiency_metrics["heat_demand"],
                }

                # For increasing variables, the new value needs to be higher than the previous, otherwise we set it to
                # the previous
                # For decreasing variables, the new value should be lower than the previous, otherwise we set it to
                # the previous
                # In either case, we adjudge the recommendation to have had no/negligible impact
                for v in increasing_variables:
                    current_phase_values[v] = (
                        current_phase_values[v] if current_phase_values[v] > previous_phase_values[v] else
                        previous_phase_values[v]
                    )
                for v in previous_phase_values:
                    if v in decreasing_variables:
                        current_phase_values[v] = (
                            current_phase_values[v] if current_phase_values[v] < previous_phase_values[v] else
                            previous_phase_values[v]
                        )

                property_phase_impact = {
                    # Increasing
                    "sap": current_phase_values["sap"] - previous_phase_values["sap"],
                    # Decreasing
                    "carbon": previous_phase_values["carbon"] - current_phase_values["carbon"],
                    # Decreasing
                    "heat_demand": previous_phase_values["heat_demand"] - current_phase_values["heat_demand"],
                }

                # Prevent from being negative
                for metric in ["sap", "carbon", "heat_demand"]:
                    property_phase_impact[metric] = (
                        0 if property_phase_impact[metric] < 0 else property_phase_impact[metric]
                    )
                    if metric == "sap":
                        property_phase_impact[metric] = round(property_phase_impact[metric], 2)

                # For the moment, we cap the number of SAP points that can be achieved by LEDs at 2
                if rec["type"] == "low_energy_lighting":
                    lighting_sap_limit = LightingRecommendations.get_sap_limit(
                        property_instance.data["lighting-energy-eff"],
                        property_instance.lighting["low_energy_proportion"]
                    )

                    property_phase_impact["sap"] = min(property_phase_impact["sap"], lighting_sap_limit)
                    property_phase_impact["carbon"] = min(
                        property_phase_impact["carbon"], rec["co2_equivalent_savings"]
                    )

                if rec["type"] == "loft_insulation":
                    # When we have a loft insulation recommendation, where there is an extension and the existing
                    # amount of loft insulation is already good, we limit the SAP points
                    # By limiting here, we don't change the value in current_phase_values. This means that the
                    # future recommendations won't have an impact that is too large
                    li_sap_limit = RoofRecommendations.get_loft_insulation_sap_limit(
                        property_instance.data["roof-energy-eff"], property_instance.data["extension-count"]
                    )
                    if li_sap_limit is not None:
                        property_phase_impact["sap"] = min(property_phase_impact["sap"], li_sap_limit)

                # Insert this information into the recommendation.
                if not rec.get("survey", False):
                    rec["sap_points"] = property_phase_impact["sap"]

                rec["co2_equivalent_savings"] = property_phase_impact["carbon"]
                rec["heat_demand"] = property_phase_impact["heat_demand"]

                if (
                    (rec["sap_points"] is None) and (rec["co2_equivalent_savings"] is None) or
                    (rec["heat_demand"] is None)
                ):
                    raise ValueError("sap points, co2 or heat demand is missing")

                impact_summary.append(
                    {
                        "phase": rec["phase"],
                        "recommendation_id": rec["recommendation_id"],
                        **current_phase_values
                    }
                )

        return property_recommendations, impact_summary

    @staticmethod
    def map_descriptions_to_fuel(heating_description, hotwater_description, main_fuel_description):

        # Handle the case of community schemes
        if (heating_description == "Community scheme") or (hotwater_description == "Community scheme"):
            if main_fuel_description == "mains gas (community)":
                return {
                    "heating_fuel_type": "Natural Gas (Community Scheme)",
                    "hotwater_fuel_type": "Natural Gas (Community Scheme)",
                    "heating_cop": 1,
                    "hotwater_cop": 1
                }
            raise NotImplementedError("Handle this case")

        mapped = assumptions.DESCRIPTIONS_TO_FUEL_TYPES[heating_description]
        heating_fuel = mapped["fuel"]

        if hotwater_description in [
            "From main system", "From main system, no cylinder thermostat",
        ]:
            return {
                "heating_fuel_type": heating_fuel, "hotwater_fuel_type": heating_fuel,
                "heating_cop": mapped["cop"], "hotwater_cop": mapped["cop"]
            }

        if hotwater_description in [
            "From main system, plus solar", "From main system, plus solar, no cylinder thermostat"
        ]:
            # The fuel is
            return {
                "heating_fuel_type": heating_fuel, "hotwater_fuel_type": heating_fuel + " + Solar Thermal",
                "heating_cop": mapped["cop"], "hotwater_cop": 1
            }

        mapped_hotwater = assumptions.DESCRIPTIONS_TO_FUEL_TYPES[hotwater_description]

        return {
            "heating_fuel_type": heating_fuel, "hotwater_fuel_type": mapped_hotwater["fuel"],
            "heating_cop": mapped["cop"], "hotwater_cop": mapped_hotwater["cop"]
        }

    @classmethod
    def calculate_recommendation_tenant_savings(
        cls, property_instance, kwh_simulation_predictions, property_recommendations
    ):
        """
        This method inserts the kwh savings and the bill savings that the customer will make from the recommendations
        based on the predictions from the ML model
        :param property_instance: Instance of the Property class, for the home associated to property_id
        :param kwh_simulation_predictions: dictionary of predictions from the model apis
        :param property_recommendations: dictionary of recommendations for the property
        :return:
        """

        kwh_impact_table = kwh_simulation_predictions["heating_kwh_predictions"][
            kwh_simulation_predictions["heating_kwh_predictions"]["property_id"] == str(property_instance.id)
            ].merge(
            kwh_simulation_predictions["hotwater_kwh_predictions"].drop(
                columns=["property_id", "recommendation_id", "phase"]
            ),
            how="inner",
            on="id",
            suffixes=("_heating", "_hotwater")
        ).reset_index(drop=True)

        # We adjust this table with the kwh estimates for low energy lighting kwh values, and solar kwh estimates
        led_recommendation = pd.DataFrame([
            {
                "phase": r["phase"],
                "recommendation_id": r["recommendation_id"],
                "lighting_kwh_savings": r["kwh_savings"]
            } for recs in property_recommendations for r in recs if r["type"] == "low_energy_lighting"
        ], columns=["phase", "recommendation_id", "lighting_kwh_savings"])

        solar_recommendations = pd.DataFrame([
            {
                "phase": r["phase"],
                "recommendation_id": r["recommendation_id"],
                "solar_kwh_savings": (
                    r["initial_ac_kwh_per_year"] * assumptions.SOLAR_CONSUMPTION_PROPORTION
                ) if not r["has_battery"] else (
                    r["initial_ac_kwh_per_year"] * assumptions.SOLAR_CONSUMPTION_WITH_BATTERY_PROPORTION
                ),
            } for recs in property_recommendations for r in recs if r["type"] == "solar_pv"
        ], columns=["phase", "recommendation_id", "solar_kwh_savings"])

        # merge them on
        kwh_impact_table = kwh_impact_table.merge(
            led_recommendation, how="left", on=["phase", "recommendation_id"]
        ).merge(
            solar_recommendations, how="left", on=["phase", "recommendation_id"]
        )

        property_kwh = property_instance.energy_consumption_estimates["unadjusted"]

        kwh_impact_table = pd.concat(
            [
                pd.DataFrame(
                    [
                        {
                            "id": STARTING_DUMMY_ID_VALUE,
                            "phase": STARTING_DUMMY_ID_VALUE,
                            "recommendation_id": STARTING_DUMMY_ID_VALUE,
                            "predictions_heating": float(property_kwh["heating"]),
                            "predictions_hotwater": float(property_kwh["hot_water"]),
                        }
                    ]
                ),
                kwh_impact_table
            ]
        ).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)

        for i in range(0, len(kwh_impact_table)):
            current_phase = kwh_impact_table.loc[i, 'phase']
            previous_phase_id = (current_phase - 1) if (current_phase > 0) else -9999
            previous_phase = kwh_impact_table[kwh_impact_table['phase'] == previous_phase_id]

            if not previous_phase.empty:
                for col in ["predictions_heating", "predictions_hotwater"]:
                    if kwh_impact_table.loc[i, col] > previous_phase[col].max():
                        kwh_impact_table.loc[i, col] = previous_phase[col].max()

        # For heating system recommendations, this could result in a fuel type change so we reflect that
        fuel_mapping = pd.DataFrame([
            {
                "id": epc["id"],
                **cls.map_descriptions_to_fuel(
                    epc["mainheat-description"], epc["hotwater-description"], epc["main-fuel"]
                )
            } for epc in property_instance.updated_simulation_epcs
        ])

        fuel_mapping = pd.concat(
            [
                pd.DataFrame(
                    [
                        {
                            "id": STARTING_DUMMY_ID_VALUE,
                            **cls.map_descriptions_to_fuel(
                                property_instance.data["mainheat-description"],
                                property_instance.data["hotwater-description"],
                                property_instance.data["main-fuel"]
                            )
                        }
                    ]
                ),
                fuel_mapping
            ]
        )

        kwh_impact_table = kwh_impact_table.merge(
            fuel_mapping, how="left", on="id"
        ).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)

        if (pd.isnull(kwh_impact_table["heating_fuel_type"]).sum() or
            pd.isnull(kwh_impact_table["hotwater_fuel_type"]).sum()):
            raise Exception("Fuel type is missing")

        # We now calculate the fuel cost
        for k in ["heating", "hotwater"]:
            kwh_impact_table[f"{k}_cost"] = kwh_impact_table.apply(
                lambda x: AnnualBillSavings.calculate_recommendation_fuel_cost(
                    x[f"predictions_{k}"], x[f"{k}_fuel_type"], x[f"{k}_cop"]
                ), axis=1
            )

        # We now deduce if any of the recommendations result in a change of fuel type
        for recs in property_recommendations:
            for rec in recs:
                if rec["type"] in [
                    "mechanical_ventilation", "trickle_vents", "draught_proofing", "extension_cavity_wall_insulation"
                ]:
                    # We cannot score the impact on draught proofing
                    continue

                rec_impact = kwh_impact_table[kwh_impact_table["recommendation_id"] == rec["recommendation_id"]]
                prevous_phase_id = (rec["phase"] - 1) if (rec["phase"] > 0) else STARTING_DUMMY_ID_VALUE
                previous_phase_impact = kwh_impact_table[kwh_impact_table["phase"] == prevous_phase_id]

                if rec["type"] == "solar_pv":
                    rec["kwh_savings"] = rec_impact["solar_kwh_savings"].values[0]
                    rec["energy_cost_savings"] = (
                        rec_impact["solar_kwh_savings"].values[0] * AnnualBillSavings.ELECTRICITY_PRICE_CAP
                    )
                    continue

                heating_kwh_savings = (
                    previous_phase_impact["predictions_heating"].mean() - rec_impact["predictions_heating"].values[0]
                )
                heating_cost_savings = (
                    previous_phase_impact["heating_cost"].mean() - rec_impact["heating_cost"].values[0]
                )

                hotwater_kwh_savings = (
                    previous_phase_impact["predictions_hotwater"].mean() - rec_impact["predictions_hotwater"].values[0]
                )
                hotwater_host = (
                    previous_phase_impact["hotwater_cost"].mean() - rec_impact["hotwater_cost"].values[0]
                )

                total_kwh_savings = heating_kwh_savings + hotwater_kwh_savings
                energy_cost_savings = heating_cost_savings + hotwater_host

                if rec["type"] == "lighting":
                    # In this case, we should probably just SKIP but check when we have one!
                    raise Exception("Implement me 3")

                rec["kwh_savings"] = total_kwh_savings
                rec["energy_cost_savings"] = energy_cost_savings

        # Finally, we set the current energy bill
        # For a community scheme, there is a standing charge but it's based on the operational cost of the network
        # and therefore is likely different to the typical standing charge. This will be a cost typically defined
        # by the network operator and often a building, whose residents are on a heat network, where the building
        # operator will purchase energy from the network and re-sell it to the residents
        starting_figures = kwh_impact_table[kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE].squeeze()
        gas_standing_charge = 0
        if (
            (starting_figures["heating_fuel_type"] in ["Natural Gas", "Natural Gas (Community Scheme)"]) or
            (starting_figures["hotwater_fuel_type"] == ["Natural Gas", "Natural Gas (Community Scheme)"])
        ):
            gas_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_GAS * 365

        electricity_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_ELECTRICITY * 365

        # We return a dictionary that contains the individual costs, that can be stored to the database
        current_energy_bill = {
            "heating_cost_current": float(starting_figures["heating_cost"]),
            "hot_water_cost_current": float(starting_figures["hotwater_cost"]),
            "lighting_cost_current": float(property_instance.energy_cost_estimates["unadjusted"]["lighting"]),
            "appliances_cost_current": float(property_instance.energy_cost_estimates["unadjusted"]["appliances"]),
            "gas_standing_charge": float(gas_standing_charge),
            "electricity_standing_charge": float(electricity_standing_charge),
        }

        return current_energy_bill