mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
minor eligibility tweaks
This commit is contained in:
Khalim Conn-Kowlessar
parent
a40c1670cb
commit
1f57ed0f9e
3 changed files with 70 additions and 33 deletions
|
|
@ -833,6 +833,18 @@ def analyse_ha_32_results(results, ha32, no_house_numbers):
|
|||
results_df["warmfront_identified"]
|
||||
]
|
||||
|
||||
# Aggregates of no eco and gbis jobs identified
|
||||
n_eco = results_df["eco4_eligible"].sum()
|
||||
# Gbis is rows where eco4 is not eligible
|
||||
n_gbis = results_df[
|
||||
(results_df["gbis_eligible"] == True) & (results_df["eco4_eligible"] == False)
|
||||
]["gbis_eligible"].sum()
|
||||
|
||||
pipeline_potential = results_df[
|
||||
(results_df["warmfront_identified"] == True) | (results_df["eco4_eligible"] == True) | (
|
||||
results_df["gbis_eligible"] == True)
|
||||
]
|
||||
|
||||
success_rate = warmfront_identified["gbis_eligible"].sum() / warmfront_identified.shape[0]
|
||||
# For HA32, this is 89%
|
||||
|
||||
|
|
@ -890,8 +902,16 @@ def analyse_ha_32_results(results, ha32, no_house_numbers):
|
|||
|
||||
new_possibilities = results_df[
|
||||
(~results_df["warmfront_identified"]) &
|
||||
(results_df["gbis_eligible"] | results_df["eco4_eligible"]) &
|
||||
(results_df["tenure"] == "Rented (social)")
|
||||
(results_df["gbis_eligible"] | results_df["eco4_eligible"])
|
||||
].copy()
|
||||
|
||||
new_possibilities_eco = results_df[
|
||||
(~results_df["warmfront_identified"]) &
|
||||
(results_df["eco4_eligible"] == True)
|
||||
].copy()
|
||||
new_possibilities_gbis = results_df[
|
||||
(~results_df["warmfront_identified"]) &
|
||||
(results_df["eco4_eligible"] == False) & (results_df["gbis_eligible"] == True)
|
||||
].copy()
|
||||
|
||||
future_possibilities_eco = results_df[
|
||||
|
|
@ -959,6 +979,11 @@ def analyse_ha_15_results(results_df, ha15, no_house_numbers):
|
|||
"eligibility_classification"].value_counts()
|
||||
# For HA15 this is 50.3%
|
||||
|
||||
pipeline_potential = results_df[
|
||||
(results_df["warmfront_identified"] == True) | (results_df["eco4_eligible"] == True) | (
|
||||
results_df["gbis_eligible"] == True)
|
||||
]
|
||||
|
||||
# of the properties we identify, what is the mix of confidenc
|
||||
|
||||
missed = results_df[
|
||||
|
|
@ -977,32 +1002,32 @@ def analyse_ha_15_results(results_df, ha15, no_house_numbers):
|
|||
missed["sap"] < 69
|
||||
]
|
||||
|
||||
sap_low_enough["walls"].value_counts()
|
||||
z = ha15[ha15["row_id"].isin(sap_too_high["row_id"].values)]
|
||||
|
||||
investigate_1 = ha15[ha15["row_id"].isin(sap_too_high["row_id"])][
|
||||
["row_id", "Postcode", "Address Line 1", "Address Line 2", "Address Line 3"]]
|
||||
|
||||
investigate_2 = ha15[ha15["row_id"].isin(sap_low_enough["row_id"])][
|
||||
["row_id", "Postcode", "Address Line 1", "Address Line 2", "Address Line 3"]]
|
||||
|
||||
missed["message"].value_counts()
|
||||
# Aggregates of no eco and gbis jobs identified
|
||||
n_eco = results_df["eco4_eligible"].sum()
|
||||
# Gbis is rows where eco4 is not eligible
|
||||
n_gbis = results_df[
|
||||
(results_df["gbis_eligible"] == True) & (results_df["eco4_eligible"] == False)
|
||||
]["gbis_eligible"].sum()
|
||||
|
||||
# We now look for properties that we identified, that were not identified by Warmfront
|
||||
|
||||
new_possibilities = results_df[
|
||||
(~results_df["warmfront_identified"]) &
|
||||
((results_df["gbis_eligible"] == True) | (results_df["eco4_eligible"] == True)) &
|
||||
(results_df["tenure"] == "Rented (social)")
|
||||
((results_df["gbis_eligible"] == True) | (results_df["eco4_eligible"] == True))
|
||||
].copy()
|
||||
|
||||
new_possibilities_eco = results_df[
|
||||
(~results_df["warmfront_identified"]) &
|
||||
(results_df["eco4_eligible"] == True)
|
||||
].copy()
|
||||
|
||||
# These are future possibilityies
|
||||
new_possibilities_eco = results_df[
|
||||
future_possibilities_eco = results_df[
|
||||
(~results_df["warmfront_identified"]) &
|
||||
(results_df["eco4_eligible_future"] == True) & (~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
|
||||
].copy()
|
||||
|
||||
new_possibilities_gbis = results_df[
|
||||
future_possibilities_gbis = results_df[
|
||||
(~results_df["warmfront_identified"]) &
|
||||
(results_df["gbis_eligible_future"] == True) & (results_df["eco4_eligible_future"] == False) & (
|
||||
~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
|
||||
|
|
|
|||
|
|
@ -264,21 +264,21 @@ def get_ha_33data(data, cleaned, cleaning_data, created_at):
|
|||
|
||||
|
||||
def analyse_ha_33(results_df, data):
|
||||
results_df_social = results_df[results_df["tenure"] == "Rented (social)"]
|
||||
# results_df_social = results_df[results_df["tenure"] == "Rented (social)"]
|
||||
#
|
||||
# results_df_social["tenure"].value_counts()
|
||||
|
||||
results_df_social["tenure"].value_counts()
|
||||
data[data["row_id"].isin(results_df["row_id"].values)]["PROPERTY TYPE"].value_counts()
|
||||
|
||||
data[data["row_id"].isin(results_df_social["row_id"].values)]["PROPERTY TYPE"].value_counts()
|
||||
n_identified = (results_df["gbis_eligible"] | results_df["eco4_eligible"]).sum()
|
||||
n_eco4 = results_df["eco4_eligible"].sum()
|
||||
n_gbis = results_df[~results_df["eco4_eligible"]]["gbis_eligible"].sum()
|
||||
|
||||
n_identified = (results_df_social["gbis_eligible"] | results_df_social["eco4_eligible"]).sum()
|
||||
n_eco4 = results_df_social["eco4_eligible"].sum()
|
||||
n_gbis = results_df_social[~results_df_social["eco4_eligible"]]["gbis_eligible"].sum()
|
||||
|
||||
eco_eligibile = results_df_social[results_df_social["eco4_eligible"]]
|
||||
eco_eligibile = results_df[results_df["eco4_eligible"]]
|
||||
eco_eligibile["walls"].value_counts()
|
||||
eco_eligibile["roof"].value_counts()
|
||||
|
||||
results_df_social[results_df_social["gbis_eligible"] | results_df_social["eco4_eligible"]]["tenure"].value_counts()
|
||||
results_df[results_df["gbis_eligible"] | results_df["eco4_eligible"]]["tenure"].value_counts()
|
||||
|
||||
results_df_social["eligibility_classification"].value_counts()
|
||||
|
||||
|
|
@ -316,3 +316,11 @@ def app():
|
|||
created_at = datetime.now().isoformat()
|
||||
|
||||
results_df, _, _ = get_ha_33data(data, cleaned, cleaning_data, created_at)
|
||||
|
||||
# Read in
|
||||
import pickle
|
||||
with open("ha33_results.pickle", "rb") as f:
|
||||
data = pickle.load(f)
|
||||
results_df = pd.DataFrame(data["results"])
|
||||
scoring_data = data["scoring_data"]
|
||||
nodata = data["nodata"]
|
||||
|
|
|
|||
|
|
@ -241,15 +241,11 @@ def get_ha_4_data(data, cleaned, cleaning_data, created_at):
|
|||
|
||||
|
||||
def analyse_ha_4(results_df, data):
|
||||
results_df_social = results_df[results_df["tenure"] == "Rented (social)"]
|
||||
n_identified = (results_df["gbis_eligible"] | results_df["eco4_eligible"]).sum()
|
||||
n_eco4 = results_df["eco4_eligible"].sum()
|
||||
n_gbis = results_df[~results_df["eco4_eligible"]]["gbis_eligible"].sum()
|
||||
|
||||
results_df_social["property_type"].value_counts()
|
||||
|
||||
n_identified = (results_df_social["gbis_eligible"] | results_df_social["eco4_eligible"]).sum()
|
||||
n_eco4 = results_df_social["eco4_eligible"].sum()
|
||||
n_gbis = results_df_social[~results_df_social["eco4_eligible"]]["gbis_eligible"].sum()
|
||||
|
||||
eco_eligibile = results_df_social[results_df_social["eco4_eligible"]]
|
||||
eco_eligibile = results_df[results_df["eco4_eligible"]]
|
||||
eco_eligibile["eligibility_classification"].value_counts()
|
||||
|
||||
future_possibilities_eco = results_df[
|
||||
|
|
@ -299,3 +295,11 @@ def app():
|
|||
# "scoring_data": scoring_data,
|
||||
# "nodata": nodata
|
||||
# }, f)
|
||||
|
||||
# Read in
|
||||
# import pickle
|
||||
# with open("ha_4.pickle", "rb") as f:
|
||||
# data = pickle.load(f)
|
||||
# results_df = data["results_df"]
|
||||
# scoring_data = data["scoring_data"]
|
||||
# nodata = data["nodata"]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue