mirror of
https://github.com/Hestia-Homes/ML.git
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new carbon model with september data
This commit is contained in:
Michael Duong
parent
eb4efdbb2a
commit
2fa6a51c98
4 changed files with 41 additions and 30 deletions
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@ -13,7 +13,11 @@ RUN yum install -y gcc python3-devel gcc-c++
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# Install python packages
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COPY modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt ./requirements.txt
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RUN pip install --no-cache-dir -r ./requirements.txt
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RUN pip install uv
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RUN uv pip install -r requirements.txt --system
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# RUN pip install --no-cache-dir -r ./requirements.txt
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# Copy the project code
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COPY modules/ml-pipeline/src/pipeline ./pipeline
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@ -5,8 +5,11 @@ RUN apt-get update && apt-get install -y libgomp1 gcc python3-dev
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COPY pipeline/requirements/predictions/requirements.txt requirements.txt
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RUN pip install --upgrade pip
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RUN pip install -r requirements.txt
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RUN pip install uv
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RUN uv pip install -r requirements.txt --system
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# RUN pip install -r requirements.txt
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# Assuming in the CI/CD step, there will be a dvc pull step to get data and model, so will just need to run a single script
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COPY pipeline/ /home/pipeline/
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@ -18,10 +18,8 @@ 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/2024-05-28-19-08-25/dataset_rooms.parquet
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data_filepath: s3://retrofit-data-dev/sap_change_model/2024-10-03-22-57-23/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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@ -37,7 +35,7 @@ default:
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drop_columns: [
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"heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "sap_ending", "days_to_starting", "days_to_ending",
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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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'number_habitable_rooms', 'number_heated_rooms', 'lighting_cost_starting', 'lighting_cost_ending', 'heating_cost_starting', 'heating_cost_ending', 'hot_water_cost_starting', 'hot_water_cost_ending',]
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# retain_features: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"]
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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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@ -34,13 +34,19 @@ stages:
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- number_heated_rooms_ending
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- number_habitable_rooms
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- number_heated_rooms
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- lighting_cost_starting
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- lighting_cost_ending
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- heating_cost_starting
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- heating_cost_ending
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- hot_water_cost_starting
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- hot_water_cost_ending
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default.feature_processor.feature_processor_config.retain_features:
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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: carbon_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/2024-05-28-19-08-25/dataset_rooms.parquet
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s3://retrofit-data-dev/sap_change_model/2024-10-03-22-57-23/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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@ -49,8 +55,8 @@ 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: e2efac20634b919381adfb962a42d40a.dir
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size: 36961727
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md5: f96aaa1181655a1bef313542f037b346.dir
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size: 40772097
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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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@ -61,8 +67,8 @@ 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: e2efac20634b919381adfb962a42d40a.dir
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size: 36961727
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md5: f96aaa1181655a1bef313542f037b346.dir
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size: 40772097
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nfiles: 2
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params:
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configs/build_model.yaml:
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@ -94,17 +100,17 @@ 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: d2568a3244df4d3444b6190599f74b96.dir
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size: 3661106
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md5: 821aace9a1dfb8b2adb507f4d7e6b36b.dir
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size: 3995384
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nfiles: 1
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- path: data/model/
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hash: md5
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md5: 756100e033e0bd4445a437e43f4c53af.dir
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size: 730442848
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md5: fde129c8b8610bdaecc3d28f4cfc6608.dir
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size: 751284807
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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: 3bcb3b9728521cd341eb71af109ca778
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md5: 471606cbb7d4f3e62fb94b493d3ec858
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size: 227
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generate_predictions:
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cmd: python 3_generate_predictions.py
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@ -115,13 +121,13 @@ 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: 756100e033e0bd4445a437e43f4c53af.dir
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size: 730442848
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md5: fde129c8b8610bdaecc3d28f4cfc6608.dir
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size: 751284807
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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: e2efac20634b919381adfb962a42d40a.dir
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size: 36961727
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md5: f96aaa1181655a1bef313542f037b346.dir
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size: 40772097
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nfiles: 2
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params:
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configs/settings.yaml:
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@ -133,8 +139,8 @@ stages:
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outs:
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- path: data/predictions/
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hash: md5
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md5: 09f3584d6fbd447dd2714eb2774139d5.dir
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size: 499683
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md5: 985d380681ab1f7645015a67b695b633.dir
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size: 557231
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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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@ -145,13 +151,13 @@ 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: 09f3584d6fbd447dd2714eb2774139d5.dir
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size: 499683
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md5: 985d380681ab1f7645015a67b695b633.dir
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size: 557231
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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: e2efac20634b919381adfb962a42d40a.dir
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size: 36961727
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md5: f96aaa1181655a1bef313542f037b346.dir
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size: 40772097
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nfiles: 2
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params:
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configs/settings.yaml:
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@ -161,8 +167,8 @@ 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: abf8720d06f073f47501aa1172527e9e
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size: 225
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md5: 9cc5f3a42681b321c26c414589ba561e
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size: 226
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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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