mirror of
https://github.com/Hestia-Homes/ML.git
synced 2026-06-08 11:17:25 +00:00
correct the dockerignore files and test model with just tabular
This commit is contained in:
Michael Duong
parent
179c334b6e
commit
620c1d10a1
9 changed files with 55 additions and 80 deletions
9
.dockerignore
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.dockerignore
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modules/ml-pipeline/src/pipeline/data/predictions
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modules/ml-pipeline/src/pipeline/data/fit_predictions
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modules/ml-pipeline/src/pipeline/data/prepared_data
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modules/ml-pipeline/src/pipeline/data/model/allmodels
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modules/ml-pipeline/src/pipeline/metrics
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modules/ml-pipeline/src/pipeline/__pycache__
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modules/ml-pipeline/src/pipeline/.dvc
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modules/ml-pipeline/src/pipeline/analysis
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modules/ml-pipeline/src/pipeline/metrics
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@ -1,4 +1,8 @@
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modules/ml-pipeline/src/pipeline/data/predictions*
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modules/ml-pipeline/src/pipeline/data/prepared_data*
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modules/ml-pipeline/src/pipeline/data/model/allmodels*
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modules/ml-pipeline/src/pipeline/metrics*
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modules/ml-pipeline/src/pipeline/data/predictions
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modules/ml-pipeline/src/pipeline/data/prepared_data
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modules/ml-pipeline/src/pipeline/data/model/allmodels
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modules/ml-pipeline/src/pipeline/metrics
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modules/ml-pipeline/src/__pycache__
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modules/ml-pipeline/src/.dvc
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modules/ml-pipeline/src/analysis
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modules/ml-pipeline/src/metrics
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@ -1,4 +1,6 @@
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pipeline/data/predictions*
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pipeline/data/prepared_data/train.parquet*
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pipeline/data/model/allmodels*
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pipeline/metrics*
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pipeline/data/predictions
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pipeline/data/prepared_data/train.parquet
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pipeline/data/model/allmodels
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pipeline/metrics
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pipeline/.dvc
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pipeline/analysis
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@ -1,7 +1,7 @@
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# Dockerfile that can be used to test loading a model to generate a prediction (part of CI/CD flow)
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FROM python:3.10.12-slim
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RUN apt-get update && apt-get install -y libgomp1
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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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@ -35,46 +35,6 @@ stages:
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- number_habitable_rooms
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- number_heated_rooms
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default.feature_processor.feature_processor_config.retain_features:
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- uprn
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- sap_starting
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- hot_water_energy_eff_ending
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- mainheat_energy_eff_ending
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- constituency
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- roof_energy_eff_ending
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- walls_energy_eff_ending
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- secondheat_description_ending
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- property_type
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- mainheatc_energy_eff_ending
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- built_form
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- walls_insulation_thickness_ending
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- 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
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- heat_demand_starting
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- photo_supply_ending
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- 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
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- 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
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- construction_age_band
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- glazed_type_ending
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- roof_thermal_transmittance_ending
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- floor_insulation_thickness_ending
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- has_mains_gas_ending
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- estimated_perimeter_starting
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- energy_consumption_potential
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- environment_impact_potential
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- heater_type_ending
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- multi_glaze_proportion_ending
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- lighting_energy_eff_ending
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- fixed_lighting_outlets_count
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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: sap_ending
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@ -89,8 +49,8 @@ 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: efa416abea618ae6220a0c3d597603cf.dir
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size: 44750997
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md5: a1ff276b1cbd2db0b8e2982cfd524b40.dir
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size: 44713582
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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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@ -101,8 +61,8 @@ 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: efa416abea618ae6220a0c3d597603cf.dir
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size: 44750997
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md5: a1ff276b1cbd2db0b8e2982cfd524b40.dir
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size: 44713582
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nfiles: 2
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params:
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configs/build_model.yaml:
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@ -134,18 +94,18 @@ 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: de46250d454c4d713ab580b10ff3fd31.dir
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size: 3349318
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md5: 750cd7426e2909ed36bc05601b5e04c4.dir
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size: 3349190
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nfiles: 1
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- path: data/model/
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hash: md5
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md5: 18bd7a93ece75a65d3a950b7dfdab4fb.dir
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size: 735951861
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nfiles: 35
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md5: aa5eb60be553f2a01e63783cf8f1fad1.dir
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size: 765461992
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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: 8a952a5e884c268e6059357a627b9251
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size: 224
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md5: ca5190b3292210c57a58668fdb48296c
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size: 226
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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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@ -155,13 +115,13 @@ 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: 18bd7a93ece75a65d3a950b7dfdab4fb.dir
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size: 735951861
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nfiles: 35
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md5: aa5eb60be553f2a01e63783cf8f1fad1.dir
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size: 765461992
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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: efa416abea618ae6220a0c3d597603cf.dir
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size: 44750997
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md5: a1ff276b1cbd2db0b8e2982cfd524b40.dir
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size: 44713582
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nfiles: 2
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params:
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configs/settings.yaml:
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outs:
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- path: data/predictions/
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hash: md5
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md5: 07ef721a0dc94a52e3ba7a70ac45b8ff.dir
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size: 463563
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md5: 934c94e5b1a2c70db3dc865ee056f460.dir
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size: 463619
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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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@ -185,13 +145,13 @@ 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: 07ef721a0dc94a52e3ba7a70ac45b8ff.dir
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size: 463563
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md5: 934c94e5b1a2c70db3dc865ee056f460.dir
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size: 463619
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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: efa416abea618ae6220a0c3d597603cf.dir
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size: 44750997
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md5: a1ff276b1cbd2db0b8e2982cfd524b40.dir
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size: 44713582
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nfiles: 2
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params:
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configs/settings.yaml:
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outs:
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- path: metrics/metrics.json
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hash: md5
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md5: 9f863f47799d42c101eba3b03a179455
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md5: aa2a511ac759225549636ba05d6b667c
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size: 224
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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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- path: 5_generate_scenarios.py
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hash: md5
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md5: a18f6c6ae2082f038df47386cf3e418e
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size: 4896
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md5: 40506749fefd926d47c60ff5b16db307
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size: 5337
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params:
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configs/scenarios.yaml:
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default.scenarios:
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outs:
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- path: metrics/scenario_metrics.md
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hash: md5
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md5: 64e7db945ff655ae03c20c9845f19106
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md5: 4085a6ea3d044ad2fe7ac63b0a685fed
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size: 363
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- path: metrics/scenario_table.md
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hash: md5
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md5: d4f8afe07b774374aeaa48f1b7b8a5fc
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md5: a2b3da77921b5dcc10f7068646e0eae3
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size: 2133
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joblib==1.3.2
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boto3==1.28.17
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pandas==2.1.4
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autogluon==1.0.0
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autogluon.tabular[lightgbm,xgboost,fastai]==1.0.0
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dynaconf==3.2.1
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pyarrow==13.0.0
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pre-commit==3.3.3
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joblib==1.3.2
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boto3==1.28.17
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pandas==2.1.4
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autogluon==1.0.0
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autogluon.tabular[lightgbm,xgboost,fastai]==1.0.0
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dynaconf==3.2.1
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pyarrow==13.0.0
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PyYAML==6.0.1
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joblib==1.3.2
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boto3==1.28.17
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pandas==2.1.4
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autogluon==1.0.0
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autogluon.tabular[lightgbm,xgboost,fastai]==1.0.0
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ray==2.6.3
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dynaconf==3.2.1
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alibi==0.9.5
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boto3==1.28.41
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pandas==2.1.4
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autogluon==1.0.0
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autogluon.tabular[lightgbm,xgboost,fastai]==1.0.0
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dynaconf==3.2.1
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