.dockerignore

@@ -1,9 +0,0 @@
-modules/ml-pipeline/src/pipeline/data/predictions
-modules/ml-pipeline/src/pipeline/data/fit_predictions
-modules/ml-pipeline/src/pipeline/data/prepared_data
-modules/ml-pipeline/src/pipeline/data/model/allmodels
-modules/ml-pipeline/src/pipeline/metrics
-modules/ml-pipeline/src/pipeline/__pycache__
-modules/ml-pipeline/src/pipeline/.dvc
-modules/ml-pipeline/src/pipeline/analysis
-modules/ml-pipeline/src/pipeline/metrics

.github/workflows/Deploy.yml

@@ -19,8 +19,8 @@ jobs:
 
       - name: Install Serverless and plugins
         run: |
-          npm install -g serverless@^3.38.0
+          npm install -g serverless
-          npm install -g serverless-domain-manager@^7.3.8
+          npm install -g serverless-domain-manager
 
       - name: Install DVC
         run: |

MODEL_REGISTRY.md

@@ -8,25 +8,25 @@
         "active": true
     },
     "sap": {
-        "version": "v0.14.0",
+        "version": "v0.10.1",
         "stage": {
-            "dev": "v0.14.0"
+            "dev": "v0.10.1"
         },
         "registered": true,
         "active": true
     },
     "heat": {
-        "version": "v0.5.0",
+        "version": "v0.4.0",
         "stage": {
-            "dev": "v0.5.0"
+            "dev": "v0.4.0"
         },
         "registered": true,
         "active": true
     },
     "carbon": {
-        "version": "v0.5.0",
+        "version": "v0.4.0",
         "stage": {
-            "dev": "v0.5.0"
+            "dev": "v0.4.0"
         },
         "registered": true,
         "active": true

deployment/.dockerignore

@@ -1,9 +1,4 @@
-modules/ml-pipeline/src/pipeline/data/predictions
+modules/ml-pipeline/src/pipeline/data/predictions*
-modules/ml-pipeline/src/pipeline/data/fit_predictions
+modules/ml-pipeline/src/pipeline/data/prepared_data*
-modules/ml-pipeline/src/pipeline/data/prepared_data
+modules/ml-pipeline/src/pipeline/data/model/allmodels*
-modules/ml-pipeline/src/pipeline/data/model/allmodels
+modules/ml-pipeline/src/pipeline/metrics*
-modules/ml-pipeline/src/pipeline/metrics
-modules/ml-pipeline/src/__pycache__
-modules/ml-pipeline/src/.dvc
-modules/ml-pipeline/src/analysis
-modules/ml-pipeline/src/metrics

modules/ml-pipeline/src/.dockerignore

@@ -1,8 +1,4 @@
-pipeline/data/predictions
+pipeline/data/predictions*
-pipeline/data/fit_predictions
+pipeline/data/prepared_data/train.parquet*
-pipeline/data/prepared_data/train.parquet
+pipeline/data/model/allmodels*
-pipeline/data/fit_predictions
+pipeline/metrics*
-pipeline/data/model/allmodels
-pipeline/metrics
-pipeline/.dvc
-pipeline/analysis

modules/ml-pipeline/src/Prediction.Dockerfile

@@ -1,7 +1,7 @@
 # Dockerfile that can be used to test loading a model to generate a prediction (part of CI/CD flow)
 FROM python:3.10.12-slim
 
-RUN apt-get update && apt-get install -y libgomp1 gcc python3-dev
+RUN apt-get update && apt-get install -y libgomp1
 
 COPY pipeline/requirements/predictions/requirements.txt requirements.txt
 

modules/ml-pipeline/src/pipeline/5_generate_scenarios.py

@@ -78,18 +78,6 @@ def generate_scenario_predictions(
 
     scenario_data = pd.DataFrame()
 
-    # If we have no scenario data, we can save empty dataframes
-    if scenario_data_filepaths is None:
-        logger.info("No scenario data filepaths provided")
-        output_dataclient.save_data(
-            obj=scenario_data, location=comparison_output_filepath, save_config=None
-        )
-
-        output_dataclient.save_data(
-            obj=scenario_data, location=metrics_output_filepath, save_config=None
-        )
-        return
-
     # Can have multiple scenario data files
     for scenario_data_filepath in scenario_data_filepaths:
         scenario_data = pd.concat(

modules/ml-pipeline/src/pipeline/configs/scenarios.yaml

@@ -5,9 +5,6 @@ default:
     scenario_data_filepaths:
       # - s3://retrofit-data-dev/scenario_data/22-03-2024-19-20-09/recommendations_scoring_data.parquet
       # - s3://retrofit-data-dev/scenario_data/24-03-2024-20-23-25/recommendations_scoring_data.parquet
-      # - s3://retrofit-data-dev/scenario_data/27-03-2024-11-38-15/recommendations_scoring_data.parquet
+      - s3://retrofit-data-dev/scenario_data/27-03-2024-11-38-15/recommendations_scoring_data.parquet
-      # - s3://retrofit-data-dev/scenario_data/26-05-2024-08-47-45/recommendations_scoring_data.parquet
-      # - s3://retrofit-data-dev/scenario_data/26-05-2024-10-44-53/recommendations_scoring_data.parquet
-      - s3://retrofit-data-dev/scenario_data/28-05-2024-19-22-41/recommendations_scoring_data.parquet
     comparison_output_filepath: ./metrics/scenario_table.md
     metrics_output_filepath: ./metrics/scenario_metrics.md

modules/ml-pipeline/src/pipeline/configs/settings.yaml

@@ -18,10 +18,7 @@ default:
   prepare_data:
     input_dataclient_type: aws-s3
     output_dataclient_type: local
-    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet
+    data_filepath: s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet
-    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-25-08-36-36/dataset_rooms.parquet
-    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-26-10-31-39/dataset_rooms.parquet
-    data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.parquet
     train_proportion: 0.9
     output_train_filepath: ./data/prepared_data/train.parquet
     output_test_filepath: ./data/prepared_data/test.parquet

modules/ml-pipeline/src/pipeline/dvc.lock

@@ -35,12 +35,52 @@ stages:
         - number_habitable_rooms
         - number_heated_rooms
         default.feature_processor.feature_processor_config.retain_features:
+        - uprn
+        - sap_starting
+        - hot_water_energy_eff_ending
+        - mainheat_energy_eff_ending
+        - constituency
+        - roof_energy_eff_ending
+        - walls_energy_eff_ending
+        - secondheat_description_ending
+        - property_type
+        - mainheatc_energy_eff_ending
+        - built_form
+        - walls_insulation_thickness_ending
+        - potential_energy_efficiency
+        - transaction_type_ending
+        - floor_thermal_transmittance_ending
+        - low_energy_lighting_ending
+        - heat_demand_starting
+        - photo_supply_ending
+        - carbon_starting
+        - walls_thermal_transmittance_ending
+        - roof_insulation_thickness_ending
+        - total_floor_area_ending
+        - number_open_fireplaces_ending
+        - windows_energy_eff_ending
+        - floor_height_ending
+        - extension_count_ending
+        - has_air_source_heat_pump_ending
+        - charging_system_ending
+        - construction_age_band
+        - glazed_type_ending
+        - roof_thermal_transmittance_ending
+        - floor_insulation_thickness_ending
+        - has_mains_gas_ending
+        - estimated_perimeter_starting
+        - energy_consumption_potential
+        - environment_impact_potential
+        - heater_type_ending
+        - multi_glaze_proportion_ending
+        - lighting_energy_eff_ending
+        - fixed_lighting_outlets_count
         default.feature_processor.feature_processor_config.subsample_amount:
         default.feature_processor.feature_processor_config.subsample_seed: 0
         default.feature_processor.feature_processor_config.target: sap_ending
         default.feature_processor.feature_processor_type: dataframe
         default.prepare_data.data_filepath:
-          s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.parquet
+          s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet
         default.prepare_data.input_dataclient_type: aws-s3
         default.prepare_data.output_dataclient_type: local
         default.prepare_data.output_test_filepath: ./data/prepared_data/test.parquet
@@ -49,8 +89,8 @@ stages:
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      md5: efa416abea618ae6220a0c3d597603cf.dir
-      size: 45056059
+      size: 44750997
       nfiles: 2
   build_model:
     cmd: python 2_build_model.py
@@ -61,8 +101,8 @@ stages:
       size: 4820
     - path: data/prepared_data
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      md5: efa416abea618ae6220a0c3d597603cf.dir
-      size: 45056059
+      size: 44750997
       nfiles: 2
     params:
       configs/build_model.yaml:
@@ -94,17 +134,17 @@ stages:
     outs:
     - path: data/fit_predictions/
       hash: md5
-      md5: d9c9afc05e8780db47c0548b19bf7d19.dir
+      md5: de46250d454c4d713ab580b10ff3fd31.dir
-      size: 3349989
+      size: 3349318
       nfiles: 1
     - path: data/model/
       hash: md5
-      md5: 13c3100e1486c27a83a8a47491077842.dir
+      md5: 18bd7a93ece75a65d3a950b7dfdab4fb.dir
-      size: 773523079
+      size: 735951861
-      nfiles: 36
+      nfiles: 35
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: 2ff70a2a45813e1bcdf2ea3aa8e07d4a
+      md5: 8a952a5e884c268e6059357a627b9251
       size: 224
   generate_predictions:
     cmd: python 3_generate_predictions.py
@@ -115,13 +155,13 @@ stages:
       size: 2464
     - path: data/model
       hash: md5
-      md5: 13c3100e1486c27a83a8a47491077842.dir
+      md5: 18bd7a93ece75a65d3a950b7dfdab4fb.dir
-      size: 773523079
+      size: 735951861
-      nfiles: 36
+      nfiles: 35
     - path: data/prepared_data
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      md5: efa416abea618ae6220a0c3d597603cf.dir
-      size: 45056059
+      size: 44750997
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -133,8 +173,8 @@ stages:
     outs:
     - path: data/predictions/
       hash: md5
-      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
+      md5: 07ef721a0dc94a52e3ba7a70ac45b8ff.dir
-      size: 463197
+      size: 463563
       nfiles: 1
   generate_metrics:
     cmd: python 4_generate_metrics.py
@@ -145,13 +185,13 @@ stages:
       size: 3484
     - path: data/predictions
       hash: md5
-      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
+      md5: 07ef721a0dc94a52e3ba7a70ac45b8ff.dir
-      size: 463197
+      size: 463563
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      md5: efa416abea618ae6220a0c3d597603cf.dir
-      size: 45056059
+      size: 44750997
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -161,30 +201,30 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: 3e08df02fd5c5d094bcf936e1338d596
+      md5: 9f863f47799d42c101eba3b03a179455
-      size: 223
+      size: 224
   generate_scenerio_metrics:
     cmd: python 5_generate_scenarios.py
     deps:
     - path: 5_generate_scenarios.py
       hash: md5
-      md5: 40506749fefd926d47c60ff5b16db307
+      md5: a18f6c6ae2082f038df47386cf3e418e
-      size: 5337
+      size: 4896
     params:
       configs/scenarios.yaml:
         default.scenarios:
           input_dataclient_type: aws-s3
           output_dataclient_type: local
           scenario_data_filepaths:
-          - s3://retrofit-data-dev/scenario_data/28-05-2024-19-22-41/recommendations_scoring_data.parquet
+          - s3://retrofit-data-dev/scenario_data/27-03-2024-11-38-15/recommendations_scoring_data.parquet
           comparison_output_filepath: ./metrics/scenario_table.md
           metrics_output_filepath: ./metrics/scenario_metrics.md
     outs:
     - path: metrics/scenario_metrics.md
       hash: md5
-      md5: fa4d6d7bbd7818613800da5f8f37ea96
+      md5: 64e7db945ff655ae03c20c9845f19106
       size: 363
     - path: metrics/scenario_table.md
       hash: md5
-      md5: d6baf100a1623cc2467c2f8221d314c9
+      md5: d4f8afe07b774374aeaa48f1b7b8a5fc
       size: 2133

modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt

@@ -1,7 +1,7 @@
 joblib==1.3.2
 boto3==1.28.17
 pandas==2.1.4
-autogluon.tabular[all]==1.0.0
+autogluon==1.0.0
 dynaconf==3.2.1
 pyarrow==13.0.0
 pre-commit==3.3.3

modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt

@@ -1,7 +1,7 @@
 joblib==1.3.2
 boto3==1.28.17
 pandas==2.1.4
-autogluon.tabular[all]==1.0.0
+autogluon==1.0.0
 dynaconf==3.2.1
 pyarrow==13.0.0
 PyYAML==6.0.1

modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt

@@ -1,7 +1,7 @@
 joblib==1.3.2
 boto3==1.28.17
 pandas==2.1.4
-autogluon.tabular[all]==1.0.0
+autogluon==1.0.0
 ray==2.6.3
 dynaconf==3.2.1
 alibi==0.9.5

modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt

@@ -1,4 +1,4 @@
 boto3==1.28.41
 pandas==2.1.4
-autogluon.tabular[all]==1.0.0
+autogluon==1.0.0
 dynaconf==3.2.1

modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt

@@ -1,4 +1,4 @@
-dvc==3.51.0
+dvc==3.36.0
-dvc-s3==3.2.0
+dvc-s3==3.0.1
-gto==1.7.1
+gto==1.6.1
 pyOpenSSL==23.3.0