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https://github.com/Hestia-Homes/ML.git
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new heating cost model, with newer data, days_to removed, lodgement year and month added
This commit is contained in:
Michael Duong
parent
29c46a3034
commit
ca0f654656
3 changed files with 42 additions and 59 deletions
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@ -5,7 +5,7 @@ During the feature processor step, we can apply additional business logic and fe
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"""
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Business Logic dict + functions
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"""
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import pandas as pd
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def remove_starting_columns(df):
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keep_column_index = [
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@ -44,6 +44,20 @@ def keep_non_zero_rdsap(df):
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df = df[df["rdsap_change"] != 0]
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return df
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def bin_photo_supply(df):
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# Define the bins in increments of 5
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bins = [-1, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100]
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# bins = [-1, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
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# Define the labels of increments of 5
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labels = ['0', '1-5', '6-10', '11-15', '16-20', '21-25', '26-30', '31-35', '36-40', '41-45', '46-50', '51-55', '56-60', '61-65', '66-70', '71-75', '76-80', '81-85', '86-90', '91-95', '96-100']
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# labels = ['0', '1-10', '11-20', '21-30', '31-40', '41-50', '51-60', '61-70', '71-80', '81-90', '91-100']
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# Create a new column with the binned data
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df['photo_supply_starting'] = pd.cut(df['photo_supply_starting'], bins=bins, labels=labels)
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df['photo_supply_ending'] = pd.cut(df['photo_supply_ending'], bins=bins, labels=labels)
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return df
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# def keep_ending_columns(df):
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# ending_column_index = [ col_name.endswith("_ENDING") for col_name in list(df.columns)]
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@ -54,12 +68,7 @@ def keep_non_zero_rdsap(df):
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# return df
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business_logic = {
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# "keep_non_zero_rdsap": keep_non_zero_rdsap,
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# "keep_flats": keep_flats,
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# "remove_minimum_habitable_room_size": remove_minimum_habitable_room_size,
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# "remove_floor_height_ending": remove_floor_height_ending
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# "remove_starting_columns": remove_starting_columns
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# "keep_ENDING_COLUMNS": keep_ending_columns
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# "bin_photo_supply": bin_photo_supply
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}
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"""
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@ -18,11 +18,7 @@ default:
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prepare_data:
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input_dataclient_type: aws-s3
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output_dataclient_type: local
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# data_filepath: s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet
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# data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-25-08-36-36/dataset_rooms.parquet
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# data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-26-10-31-39/dataset_rooms.parquet
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# data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.parquet
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data_filepath: s3://retrofit-data-dev/sap_change_model/2024-07-03-23-11-39/dataset_rooms.parquet
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data_filepath: s3://retrofit-data-dev/sap_change_model/2024-08-06-11-19-49/dataset_rooms.parquet
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train_proportion: 0.9
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output_train_filepath: ./data/prepared_data/train.parquet
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output_test_filepath: ./data/prepared_data/test.parquet
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@ -34,37 +30,13 @@ default:
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subsample_seed: 0
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target: heating_cost_ending
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identifier_columns: ["uprn"]
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# drop_columns: ["heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", "days_to_starting", "days_to_ending"]
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drop_columns: [
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"sap_ending", "heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending",
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"lighting_cost_ending", "hot_water_cost_ending",
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# "days_to_starting", "days_to_ending",
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"days_to_ending", "days_to_starting",
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'number_habitable_rooms_starting', 'number_habitable_rooms_ending', 'number_heated_rooms_starting', 'number_heated_rooms_ending',
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'number_habitable_rooms', 'number_heated_rooms']
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retain_features: null
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# retain_features: ['uprn', 'sap_starting', 'hot_water_energy_eff_ending',
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# 'mainheat_energy_eff_ending', 'constituency', 'roof_energy_eff_ending',
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# 'walls_energy_eff_ending', 'secondheat_description_ending',
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# 'property_type', 'mainheatc_energy_eff_ending', 'built_form',
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# 'walls_insulation_thickness_ending', 'potential_energy_efficiency',
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# 'transaction_type_ending',
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# 'floor_thermal_transmittance_ending',
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# 'low_energy_lighting_ending', 'heat_demand_starting',
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# 'photo_supply_ending', 'carbon_starting',
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# 'walls_thermal_transmittance_ending',
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# 'roof_insulation_thickness_ending',
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# 'total_floor_area_ending', 'number_open_fireplaces_ending',
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# 'windows_energy_eff_ending',
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# 'floor_height_ending',
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# 'extension_count_ending',
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# 'has_air_source_heat_pump_ending',
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# 'charging_system_ending', 'construction_age_band', 'glazed_type_ending',
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# 'roof_thermal_transmittance_ending',
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# 'floor_insulation_thickness_ending', 'has_mains_gas_ending',
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# 'estimated_perimeter_starting', 'energy_consumption_potential',
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# 'environment_impact_potential', 'heater_type_ending',
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# 'multi_glaze_proportion_ending',
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# 'lighting_energy_eff_ending', 'fixed_lighting_outlets_count']
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generate_predictions:
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input_dataclient_type: local
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@ -29,6 +29,8 @@ stages:
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- carbon_ending
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- lighting_cost_ending
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- hot_water_cost_ending
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- days_to_ending
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- days_to_starting
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- number_habitable_rooms_starting
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- number_habitable_rooms_ending
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- number_heated_rooms_starting
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@ -41,7 +43,7 @@ stages:
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default.feature_processor.feature_processor_config.target: heating_cost_ending
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default.feature_processor.feature_processor_type: dataframe
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default.prepare_data.data_filepath:
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s3://retrofit-data-dev/sap_change_model/2024-07-03-23-11-39/dataset_rooms.parquet
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s3://retrofit-data-dev/sap_change_model/2024-08-06-11-19-49/dataset_rooms.parquet
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default.prepare_data.input_dataclient_type: aws-s3
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default.prepare_data.output_dataclient_type: local
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default.prepare_data.output_test_filepath: ./data/prepared_data/test.parquet
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@ -50,8 +52,8 @@ stages:
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outs:
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- path: data/prepared_data/
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hash: md5
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md5: c470ebd5aad4f86b99de53ae4778daac.dir
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size: 49274194
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md5: 13346c4e055a5ffdb182ec25c8c58544.dir
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size: 53753539
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nfiles: 2
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build_model:
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cmd: python 2_build_model.py
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@ -62,8 +64,8 @@ stages:
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size: 4820
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- path: data/prepared_data
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hash: md5
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md5: c470ebd5aad4f86b99de53ae4778daac.dir
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size: 49274194
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md5: 13346c4e055a5ffdb182ec25c8c58544.dir
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size: 53753539
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nfiles: 2
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params:
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configs/build_model.yaml:
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@ -95,18 +97,18 @@ stages:
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outs:
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- path: data/fit_predictions/
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hash: md5
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md5: 41bd7d095c49076861fc242121173c9e.dir
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size: 3445925
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md5: 9c13222c2db372b376d7e7d3f778cf9b.dir
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size: 3550468
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nfiles: 1
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- path: data/model/
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hash: md5
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md5: 03fc0b07da59a7f1ba6d2fbb5502ae90.dir
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size: 780161990
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md5: c653c6d2eedb97aeea67ff8a15af825a.dir
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size: 832419878
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nfiles: 36
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- path: metrics/fit_metrics.json
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hash: md5
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md5: dc94170c8db0bde6239046def42e616e
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size: 225
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md5: 45e1e99b6125b9c97978402c62165cf6
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size: 221
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generate_predictions:
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cmd: python 3_generate_predictions.py
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deps:
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@ -116,13 +118,13 @@ stages:
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size: 2464
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- path: data/model
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hash: md5
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md5: 03fc0b07da59a7f1ba6d2fbb5502ae90.dir
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size: 780161990
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md5: c653c6d2eedb97aeea67ff8a15af825a.dir
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size: 832419878
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nfiles: 36
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- path: data/prepared_data
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hash: md5
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md5: c470ebd5aad4f86b99de53ae4778daac.dir
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size: 49274194
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md5: 13346c4e055a5ffdb182ec25c8c58544.dir
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size: 53753539
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nfiles: 2
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params:
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configs/settings.yaml:
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@ -134,8 +136,8 @@ stages:
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outs:
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- path: data/predictions/
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hash: md5
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md5: 17f2f6b46e486ecce4453eaa32b80509.dir
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size: 480739
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md5: 81434d05d87171aa43b16eb20705ddcd.dir
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size: 497824
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nfiles: 1
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generate_metrics:
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cmd: python 4_generate_metrics.py
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@ -146,13 +148,13 @@ stages:
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size: 3484
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- path: data/predictions
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hash: md5
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md5: 17f2f6b46e486ecce4453eaa32b80509.dir
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size: 480739
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md5: 81434d05d87171aa43b16eb20705ddcd.dir
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size: 497824
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nfiles: 1
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- path: data/prepared_data
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hash: md5
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md5: c470ebd5aad4f86b99de53ae4778daac.dir
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size: 49274194
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md5: 13346c4e055a5ffdb182ec25c8c58544.dir
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size: 53753539
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nfiles: 2
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params:
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configs/settings.yaml:
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@ -162,7 +164,7 @@ stages:
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outs:
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- path: metrics/metrics.json
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hash: md5
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md5: 2d9cf9eb1bb35878d2be076b34bb6cf5
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md5: 370ca59a842b3ebc8adf6d1ed11314ec
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size: 220
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generate_scenerio_metrics:
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cmd: python 5_generate_scenarios.py
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