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Merge pull request #138 from Hestia-Homes/hotwaterkwh-dev-model
Hotwaterkwh dev model
This commit is contained in:
KhalimCK
GitHub
commit
bf39378e0d
3 changed files with 153 additions and 61 deletions
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@ -17,14 +17,15 @@ Within `src` folder, the structure is as follows:
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# How to develop using this pipeline:
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Run `make init`, which will:
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- Download pyenv (Python version management)
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- Download Python 3.X.X as defined in the `make` file - current 3.10.12
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- Create a virtual environment with this version of python
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First, download miniconda to use conda to manage Python Environments
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Rund `conda init`, to initialise your terminal
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Change to this directory and run `make init`, which will:
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- Create a conda virtual environment with this version of python - current 3.10.12
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- Install packages in the training and version control directories in the pipeline folder (dev version if applicable)
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- Install pre-commit to enable pre-commit hooks
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To use the environment, run `source .dev_env_pipeline/bin/activate`.
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To use the environment, run `conda activate dev_env_pipeline`
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To enable the virtual envrionemnt created in vscode:
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- Open settings
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@ -18,12 +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/energy_consumption/2024-07-08/energy_consumption_dataset.parquet
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data_filepath: s3://retrofit-data-dev/energy_consumption/2024-07-25/energy_consumption_dataset.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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@ -35,32 +30,75 @@ default:
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subsample_seed: 0
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target: hot_water_kwh
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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: ["heating_kwh"]
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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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retain_features: [
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'uprn',
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'heating-cost-current',
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'co2-emissions-current',
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'hot-water-cost-current',
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'total-floor-area',
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'secondheat-description',
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'environment-impact-current',
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'floor-description',
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'mainheat-energy-eff',
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'current-energy-efficiency',
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'mainheat-env-eff',
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'walls-energy-eff',
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'roof-energy-eff',
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'property-type',
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'mainheat-description',
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'hot-water-env-eff',
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'mechanical-ventilation',
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'floor-level',
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'built-form',
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'walls-description',
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'mainheatcont-description',
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'roof-description',
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'energy-consumption-current',
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'construction-age-band',
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'hotwater-description',
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# 'lodgement-datetime',
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'main-fuel',
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'hot-water-energy-eff',
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'co2-emiss-curr-per-floor-area',
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'windows-energy-eff',
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'current-energy-rating',
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'lodgement-year',
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'extension-count',
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'number-open-fireplaces',
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'number-heated-rooms',
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# 'lodgement-date',
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# 'number-habitable-rooms',
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'windows-description',
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# 'local-authority',
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'photo-supply',
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'heat-loss-corridor',
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# 'posttown',
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# 'address',
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'flat-top-storey',
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'unheated-corridor-length',
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'fixed-lighting-outlets-count',
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# 'inspection-date',
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'tenure',
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# 'county',
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# 'constituency-label',
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'multi-glaze-proportion',
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'solar-water-heating-flag',
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# 'address2',
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'energy-tariff',
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'floor-height',
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'constituency',
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# 'uprn-source',
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'transaction-type',
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'floor-energy-eff',
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# 'postcode',
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'lodgement-month',
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'lighting-cost-current',
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'glazed-area',
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# 'address1',
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'floor-env-eff',
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'main-heating-controls'
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]
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generate_predictions:
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input_dataclient_type: local
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@ -23,12 +23,65 @@ stages:
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default.feature_processor.feature_processor_config.drop_columns:
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- heating_kwh
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default.feature_processor.feature_processor_config.retain_features:
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- uprn
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- heating-cost-current
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- co2-emissions-current
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- hot-water-cost-current
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- total-floor-area
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- secondheat-description
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- environment-impact-current
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- floor-description
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- mainheat-energy-eff
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- current-energy-efficiency
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- mainheat-env-eff
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- walls-energy-eff
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- roof-energy-eff
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- property-type
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- mainheat-description
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- hot-water-env-eff
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- mechanical-ventilation
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- floor-level
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- built-form
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- walls-description
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- mainheatcont-description
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- roof-description
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- energy-consumption-current
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- construction-age-band
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- hotwater-description
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- main-fuel
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- hot-water-energy-eff
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- co2-emiss-curr-per-floor-area
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- windows-energy-eff
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- current-energy-rating
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- lodgement-year
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- extension-count
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- number-open-fireplaces
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- number-heated-rooms
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- windows-description
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- photo-supply
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- heat-loss-corridor
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- flat-top-storey
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- unheated-corridor-length
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- fixed-lighting-outlets-count
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- tenure
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- multi-glaze-proportion
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- solar-water-heating-flag
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- energy-tariff
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- floor-height
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- constituency
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- transaction-type
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- floor-energy-eff
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- lodgement-month
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- lighting-cost-current
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- glazed-area
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- floor-env-eff
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- main-heating-controls
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default.feature_processor.feature_processor_config.subsample_amount:
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default.feature_processor.feature_processor_config.subsample_seed: 0
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default.feature_processor.feature_processor_config.target: hot_water_kwh
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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/energy_consumption/2024-07-08/energy_consumption_dataset.parquet
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s3://retrofit-data-dev/energy_consumption/2024-07-25/energy_consumption_dataset.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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@ -37,8 +90,8 @@ 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: 322c8294651dea6c4db9e06157a91ffd.dir
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size: 23387145
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md5: 295ac4fd05a1a3373401a7318b0b5186.dir
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size: 13131853
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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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@ -49,8 +102,8 @@ 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: 322c8294651dea6c4db9e06157a91ffd.dir
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size: 23387145
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md5: 295ac4fd05a1a3373401a7318b0b5186.dir
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size: 13131853
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nfiles: 2
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params:
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configs/build_model.yaml:
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@ -82,18 +135,18 @@ 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: b149b2be5ed3105e73b02000b9912422.dir
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size: 724848
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md5: 3e48cec68f702bc822eed8fcb2c5c603.dir
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size: 1787931
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nfiles: 1
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- path: data/model/
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hash: md5
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md5: 3fe37e27b51fe6d9472252f219fd9126.dir
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size: 465478726
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nfiles: 36
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md5: 37f7480141e920c68faacd39478a1a68.dir
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size: 451364406
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nfiles: 35
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- path: metrics/fit_metrics.json
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hash: md5
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md5: c27dcce525b763fa7c2c55820ae72727
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size: 225
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md5: e7a2a5efea57b1ddd1431b713d78bb11
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size: 219
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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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@ -103,13 +156,13 @@ 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: 3fe37e27b51fe6d9472252f219fd9126.dir
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size: 465478726
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nfiles: 36
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md5: 37f7480141e920c68faacd39478a1a68.dir
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size: 451364406
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nfiles: 35
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- path: data/prepared_data
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hash: md5
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md5: 322c8294651dea6c4db9e06157a91ffd.dir
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size: 23387145
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md5: 295ac4fd05a1a3373401a7318b0b5186.dir
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size: 13131853
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nfiles: 2
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params:
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configs/settings.yaml:
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@ -121,8 +174,8 @@ stages:
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outs:
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- path: data/predictions/
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hash: md5
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md5: 07db4158559475e73ffb06ff95a6c869.dir
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size: 77435
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md5: 9267a66c6fae4da5a589faab76fac14c.dir
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size: 192482
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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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@ -133,13 +186,13 @@ 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: 07db4158559475e73ffb06ff95a6c869.dir
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size: 77435
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md5: 9267a66c6fae4da5a589faab76fac14c.dir
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size: 192482
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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: 322c8294651dea6c4db9e06157a91ffd.dir
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size: 23387145
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md5: 295ac4fd05a1a3373401a7318b0b5186.dir
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size: 13131853
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nfiles: 2
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params:
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configs/settings.yaml:
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@ -149,8 +202,8 @@ 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: db8eddb1bb0b190188e25de65bdbd8e8
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size: 220
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md5: 3a08c29f028f5e3cb50fb8cd3608e2f4
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size: 221
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generate_scenerio_metrics:
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cmd: python 5_generate_scenarios.py
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deps:
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