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Merge pull request #120 from Hestia-Homes/heat-dev-model
Heat dev model
This commit is contained in:
KhalimCK
GitHub
commit
a409accbfb
5 changed files with 70 additions and 35 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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@ -40,13 +40,34 @@ def remove_unreasonable_habitable_rooms(df):
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return df
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def remove_top_1_percent_heat_demand(df):
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def remove_top_1_percent_heat_demand_starting(df):
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# threshold_value = df.describe(percentiles=[0.99])['HEAT_DEMAND_STARTING']['99%']
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threshold_value = 860
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df = df[df["heat_demand_starting"] < threshold_value]
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return df
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def remove_negative_heat_demand_starting(df):
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# threshold_value = df.describe(percentiles=[0.99])['HEAT_DEMAND_STARTING']['99%']
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threshold_value = 0
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df = df[df["heat_demand_starting"] > threshold_value]
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return df
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# def remove_top_1_percent_heat_demand_ending(df):
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# # threshold_value = df.describe(percentiles=[0.99])['HEAT_DEMAND_STARTING']['99%']
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# threshold_value = 593
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# df = df[df["heat_demand_ending"] < threshold_value]
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# return df
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def remove_negative_heat_demand_ending(df):
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# threshold_value = df.describe(percentiles=[0.99])['HEAT_DEMAND_STARTING']['99%']
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threshold_value = 0
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df = df[df["heat_demand_ending"] > threshold_value]
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return df
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def remove_top_1_percent_carbon(df):
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# threshold_value = df.describe(percentiles=[0.99])['CARBON_STARTING']['99%']
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threshold_value = 18
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@ -66,7 +87,10 @@ business_logic = {
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"remove_unreasonable_habitable_rooms": remove_unreasonable_habitable_rooms,
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"keep_negative_heat_change": keep_negative_heat_change,
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"keep_negative_carbon_change": keep_negative_carbon_change,
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"remove_top_1_percent_heat_demand": remove_top_1_percent_heat_demand,
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"remove_top_1_percent_heat_demand": remove_top_1_percent_heat_demand_starting,
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"remove_negative_heat_demand_starting": remove_negative_heat_demand_starting,
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# "remove_top_1_percent_heat_demand_ending": remove_top_1_percent_heat_demand_ending,
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"remove_negative_heat_demand_ending": remove_negative_heat_demand_ending,
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"remove_top_1_percent_carbon": remove_top_1_percent_carbon,
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# "remove_starting_columns": remove_starting_columns
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# "keep_ENDING_COLUMNS": keep_ending_columns
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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/2024-06-09-10-36-53/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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@ -36,7 +34,7 @@ default:
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drop_columns: [
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"heat_demand_change", "carbon_change", "rdsap_change", "sap_ending", "carbon_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: heat_demand_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: 13cd955d579de20efe743f82bc434c7e.dir
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size: 37294025
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md5: ac22171e3434233359d3ee05ae82d098.dir
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size: 41096450
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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: 13cd955d579de20efe743f82bc434c7e.dir
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size: 37294025
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md5: ac22171e3434233359d3ee05ae82d098.dir
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size: 41096450
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nfiles: 2
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params:
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configs/build_model.yaml:
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@ -94,18 +100,18 @@ 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: b9c9ca64ea6973c409c3a7b8f8ed0c3e.dir
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size: 2902493
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md5: 58956584afc6939113016c1d252ec199.dir
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size: 3126151
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nfiles: 1
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- path: data/model/
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hash: md5
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md5: a9215bba342ed7ec3f97815dfef94e48.dir
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size: 727501601
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nfiles: 36
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md5: 68865aace24ff0aa9241ffcec1f465eb.dir
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size: 714713875
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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: 548a431d58cd4f5a3118235dec734372
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size: 219
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md5: 7eb0b3080018ec5a30e2ddc77c3eab91
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size: 223
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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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@ -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: a9215bba342ed7ec3f97815dfef94e48.dir
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size: 727501601
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nfiles: 36
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md5: 68865aace24ff0aa9241ffcec1f465eb.dir
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size: 714713875
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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: 13cd955d579de20efe743f82bc434c7e.dir
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size: 37294025
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md5: ac22171e3434233359d3ee05ae82d098.dir
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size: 41096450
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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: 484781d6b359e458a25e9ab728d6514d.dir
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size: 380517
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md5: 28cc6fbd43a3645ed02fc98ce51a809a.dir
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size: 426349
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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: 3447
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- path: data/predictions
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hash: md5
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md5: 484781d6b359e458a25e9ab728d6514d.dir
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size: 380517
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md5: 28cc6fbd43a3645ed02fc98ce51a809a.dir
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size: 426349
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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: 13cd955d579de20efe743f82bc434c7e.dir
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size: 37294025
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md5: ac22171e3434233359d3ee05ae82d098.dir
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size: 41096450
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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: 4d246765aff7c45079d02b4d8f7527f7
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size: 220
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md5: d80f216a55a99847174a7c44c011fe82
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size: 223
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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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