.github/workflows/MLPipelinePullRequest.yml

@@ -32,92 +32,6 @@ jobs:
   #       echo "Please choose one of these tags: 'major', 'major', 'patch'"
   #       exit(1)
 
-  Verify-Lambda:
-
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v3
-    - name: Install packages to retrieve artifacts
-      env:
-        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
-        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
-      run: |
-        pip install --upgrade pip
-        pip install -r modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
-
-    - name: Retrieve artifacts (dvc.lock)
-      env:
-        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
-        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
-      run: |
-        cd modules/ml-pipeline/src/pipeline
-        dvc pull -r experiments
-
-    - name: Set timestamp
-      id: set_timestamp
-      run: |
-        echo "timestamp=$(date +%Y%m%d)" >> $GITHUB_ENV
-        echo "Generated timestamp: ${timestamp}"
-
-    - name: Upload sample row dataset to S3
-      env:
-        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
-        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
-      run: |
-        cd modules/ml-pipeline/src/pipeline/data/prepared_data/
-        aws s3 cp sample_test.parquet s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/sample_test.parquet
-
-    - name: Build Lambda docker Image
-      run: |
-        docker build . --file ./deployment/Dockerfile.prediction.lambda --tag lambda_test
-
-    - name: Run lambda docker container
-      env:
-        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
-        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
-      run: |
-        docker run -d -p 9000:8080 \
-          -e AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID} \
-          -e AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY} \
-          -e RUNTIME_ENVIRONMENT=dev \
-          -e PREDICTIONS_BUCKET=retrofit-sap-predictions-dev lambda_test
-
-    - name: Test Lambda endpoint
-      run: |
-        sleep 2
-        curl -X POST "http://localhost:9000/2015-03-31/functions/function/invocations" \
-          -H "Content-Type: application/json" \
-          -d "{\"body\": \"{\\\"file_location\\\": \\\"s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/sample_test.parquet\\\", \\\"property_id\\\": 1, \\\"portfolio_id\\\": 4, \\\"created_at\\\": \\\"now\\\", \\\"warm\\\": true}\"}"
-
-    - name: Get Lambda logs
-      run: |
-        docker logs $(docker ps -al -q)
-
-    - name: Test Lambda endpoint again
-      run: |
-        sleep 2
-        curl -X POST "http://localhost:9000/2015-03-31/functions/function/invocations" \
-          -H "Content-Type: application/json" \
-          -d "{\"body\": \"{\\\"file_location\\\": \\\"s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/sample_test.parquet\\\", \\\"property_id\\\": 1, \\\"portfolio_id\\\": 4, \\\"created_at\\\": \\\"now\\\", \\\"testing\\\": true}\"}"
-
-    - name: Get Lambda logs
-      run: |
-        docker logs $(docker ps -al -q)
-
-    - name: Stop Lambda container
-      run: |
-        docker stop lambda_test || echo "Container already stopped"
-
-    - name: Remove uploaded sample row dataset from S3
-      if: always()
-      env:
-        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
-        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
-      run: |
-        aws s3 rm --recursive s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/
-
-
   Verify-Model:
 
     runs-on: ubuntu-latest
@@ -200,4 +114,4 @@ jobs:
         # metrics_location=$(find . -maxdepth 10 -name "residuals.png")
         # echo $metrics_location
         # cd $metric_location
-        # echo "![](./residuals.png)" >> report.md
+        # echo "![](./residuals.png)" >> report.md

MODEL_REGISTRY.md

@@ -8,65 +8,25 @@
         "active": true
     },
     "sap": {
-        "version": "v0.15.0",
+        "version": "v0.14.0",
         "stage": {
-            "dev": "v0.15.0"
+            "dev": "v0.14.0"
         },
         "registered": true,
         "active": true
     },
     "heat": {
-        "version": "v0.7.0",
+        "version": "v0.5.0",
         "stage": {
-            "dev": "v0.7.0"
+            "dev": "v0.5.0"
         },
         "registered": true,
         "active": true
     },
     "carbon": {
-        "version": "v0.7.0",
+        "version": "v0.5.0",
         "stage": {
-            "dev": "v0.7.0"
-        },
-        "registered": true,
-        "active": true
-    },
-    "hotwater": {
-        "version": "v1.0.0",
-        "stage": {
-            "dev": "v1.0.0"
-        },
-        "registered": true,
-        "active": true
-    },
-    "heating": {
-        "version": "v1.0.0",
-        "stage": {
-            "dev": "v1.0.0"
-        },
-        "registered": true,
-        "active": true
-    },
-    "lighting": {
-        "version": "v1.0.0",
-        "stage": {
-            "dev": "v1.0.0"
-        },
-        "registered": true,
-        "active": true
-    },
-    "hotwaterkwh": {
-        "version": "v1.3.0",
-        "stage": {
-            "dev": "v1.3.0"
-        },
-        "registered": true,
-        "active": true
-    },
-    "heatingkwh": {
-        "version": "v1.5.0",
-        "stage": {
-            "dev": "v1.5.0"
+            "dev": "v0.5.0"
         },
         "registered": true,
         "active": true

README.md

@@ -83,13 +83,3 @@ curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d
 ```
 
 This will send a POST request to the running Lambda function and pass in the required data as JSON.
-
-For the testing of warm or testing of the lambda, use:
-
-```json
-curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{"body": "{\"file_location\": \"s3://retrofit-data-dev/sap_change_model/one_sample_test_dataset.parquet\", \"property_id\": 1, \"portfolio_id\": 4, \"created_at\": \"now\", \"testing\": \"true\"}"}'
-```
-or
-```json
-curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{"body": "{\"file_location\": \"s3://retrofit-data-dev/sap_change_model/one_sample_test_dataset.parquet\", \"property_id\": 1, \"portfolio_id\": 4, \"created_at\": \"now\", \"warm\": \"true\"}"}'
-```

deployment/Dockerfile.prediction.lambda

@@ -1,24 +1,19 @@
-FROM public.ecr.aws/lambda/python:3.12
+FROM public.ecr.aws/lambda/python:3.10
 
 # Set the working directory
 WORKDIR ${LAMBDA_TASK_ROOT}
-ENV PYTHONPATH="${PYTHONPATH}:${LAMBDA_TASK_ROOT}"
-ENV MPLCONFIGDIR="/tmp/matplotlib"
+ENV PYTHONPATH "${PYTHONPATH}:${LAMBDA_TASK_ROOT}"
 
 # Environment variables
 ARG RUNTIME_ENVIRONMENT
 ENV RUNTIME_ENVIRONMENT=${RUNTIME_ENVIRONMENT}
 
 # Install necessary build tools - required to test locally
-RUN dnf install -y gcc python3-devel gcc-c++
+RUN yum install -y gcc python3-devel gcc-c++
 
 # Install python packages
 COPY modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt ./requirements.txt
-
-RUN pip install uv
-
-RUN uv pip install -r requirements.txt --system
-# RUN pip install --no-cache-dir -r ./requirements.txt
+RUN pip install --no-cache-dir -r ./requirements.txt
 
 # Copy the project code
 COPY modules/ml-pipeline/src/pipeline ./pipeline
@@ -27,4 +22,4 @@ COPY deployment/handlers/prediction_app.py ./pipeline/prediction_app.py
 WORKDIR ${LAMBDA_TASK_ROOT}/pipeline
 
 
-CMD [ "prediction_app.handler" ]
+CMD [ "prediction_app.handler" ]

deployment/handlers/prediction_app.py

@@ -47,30 +47,6 @@ def upload_dataframe_to_s3(df, bucket, s3_file_name):
         return False
 
 
-def warming_up_invocation(
-    model,
-    model_filepath: str,
-):
-    """
-    Function to handle warm up invocations
-    """
-    import pandas as pd
-    import numpy as np
-
-    model.load_model(model_filepath)
-
-    warmup_df = pd.DataFrame(
-        np.zeros((1, len(model.model.original_features))),
-        columns=model.model.original_features,
-    )
-
-    # model_names = model.model.model_names()
-    # if "NeuralNetFastAI" in model_names:
-    #     model.model.predict(warmup_df, model="NeuralNetFastAI")
-    # else:
-    model.predict(data=warmup_df)
-
-
 def handler(event, context):
     """
     Take in event and trigger the prediction pipeline
@@ -90,6 +66,9 @@ def handler(event, context):
         created_at = body["created_at"]
 
         # TODO: Implement the loading of the model and prediction
+
+        storage_filepath = f"s3://{PREDICTIONS_BUCKET}/{portfolio_id}/{property_id}/{created_at}.parquet"
+
         logger.info(f"--- Initiate MLModel ---")
 
         build_model_params = settings.build_model
@@ -99,32 +78,6 @@ def handler(event, context):
 
         model = model_factory(build_model_params["model_type"])
 
-        model_filepath = build_model_params["model_save_filepath"]
-
-        if "warm" in body:
-            logger.info("Warm up invocation - synthetic prediction")
-
-            warming_up_invocation(model=model, model_filepath=model_filepath)
-
-            return {
-                "statusCode": 200,
-                "body": json.dumps(
-                    {
-                        "message": "Successfully warmed up invocation",
-                    }
-                ),
-            }
-
-        if "testing" in body:
-            logger.info(
-                "Testing invocation for CI/CD - save file to same location in S3"
-            )
-            storage_filepath = body["file_location"].replace(
-                ".parquet", "_output.parquet"
-            )
-        else:
-            storage_filepath = f"s3://{PREDICTIONS_BUCKET}/{portfolio_id}/{property_id}/{created_at}.parquet"
-
         logger.info(f"--- Initiate Input DataClient ---")
         input_dataclient = dataclient_factory(
             dataclient_type="aws-s3",
@@ -142,7 +95,7 @@ def handler(event, context):
             output_dataclient=output_dataclient,
             model=model,
             target=feature_process_params["feature_processor_config"]["target"],
-            model_filepath=model_filepath,
+            model_filepath=build_model_params["model_save_filepath"],
             test_data_filepath=body["file_location"],
             predictions_output_filepath=storage_filepath,
             predictions_column_name=generate_predictions_params[

deployment/serverless.yml

@@ -51,4 +51,3 @@ functions:
           path: /predict
           method: POST
     timeout: 120 # Set max run time to 2 minutes - we shouldn't need this much time so this can be reviewed
-    memorySize: 3008

modules/ml-pipeline/Makefile

@@ -1,8 +1,7 @@
 export PYENV_ROOT=$(HOME)/.pyenv
 export PATH := $(PYENV_ROOT)/bin:$(PATH)
-PYTHON_VERSION ?= 3.12.12
+PYTHON_VERSION ?= 3.10.12
 CONDA_ENV=dev_env_pipeline
-CONDA_ACTIVATE=source $$(conda info --base)/etc/profile.d/conda.sh ; conda deactivate ; conda activate
 
 .PHONY: init
 init: dev-conda
@@ -13,15 +12,11 @@ dev-conda:
 	# conda remove --name ${CONDA_ENV} --all -y || echo "No environment created previously"
 	conda create --name ${CONDA_ENV} python=$(PYTHON_VERSION) -y
 	conda init bash
-	${CONDA_ACTIVATE} ${CONDA_ENV} && \
-		which pip && \
-		pip install --upgrade pip && \
-		pip install uv && \
-		uv pip install -r src/pipeline/requirements/training/requirements-dev.txt && \
-		uv pip install -r src/pipeline/requirements/version_control/requirements.txt && \
-		pre-commit install && \
-		uv pip install ipykernel && \
-		conda install llvm-openmp -y
+	conda run -v -n ${CONDA_ENV} pip install --upgrade pip
+	conda run -v -n ${CONDA_ENV} pip install -r src/pipeline/requirements/training/requirements-dev.txt
+	conda run -v -n ${CONDA_ENV} pip install -r src/pipeline/requirements/version_control/requirements.txt
+	conda run -v -n ${CONDA_ENV} pre-commit install
+	conda run -v -n ${CONDA_ENV} pip install ipykernel
 	echo "TO ACTIVATE ENVIRONMENT, USE THE FOLLOWING COMMAND"
 	echo "conda activate ${CONDA_ENV}"
 
@@ -38,4 +33,4 @@ dev-pyenv:
 
 .PHONY: dvc-init
 dvc-init:
-	. .dev_env_pipeline/bin/activate && dvc init --subdir
+	. .dev_env_pipeline/bin/activate && dvc init --subdir

modules/ml-pipeline/src/Prediction.Dockerfile

@@ -1,21 +1,16 @@
 # Dockerfile that can be used to test loading a model to generate a prediction (part of CI/CD flow)
-FROM python:3.12.12-slim
+FROM python:3.10.12-slim
 
 RUN apt-get update && apt-get install -y libgomp1 gcc python3-dev
 
 COPY pipeline/requirements/predictions/requirements.txt requirements.txt
 
 RUN pip install --upgrade pip
-
-RUN pip install uv
-
-RUN uv pip install -r requirements.txt --system
-
-# RUN pip install -r requirements.txt
+RUN pip install -r requirements.txt
 
 # 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
 COPY pipeline/ /home/pipeline/
 
 WORKDIR /home/pipeline/
 
-CMD [ "python", "3_generate_predictions.py"]
+CMD [ "python", "3_generate_predictions.py"]

modules/ml-pipeline/src/pipeline/1_prepare_data.py

@@ -29,7 +29,6 @@ data_filepath = prepare_data_params["data_filepath"]
 train_proportion = prepare_data_params["train_proportion"]
 output_train_filepath = prepare_data_params["output_train_filepath"]
 output_test_filepath = prepare_data_params["output_test_filepath"]
-sample_test_filepath = prepare_data_params["sample_test_filepath"]
 feature_processor_config = feature_process_params["feature_processor_config"]
 
 logger.info(f"--- Initiate DataClient ---")
@@ -100,10 +99,6 @@ def prepare_data(
 
     logger.info("--- Outputting data ---")
 
-    output_dataclient.save_data(
-        obj=data.sample(1), location=sample_test_filepath, save_config=None
-    )
-
     output_dataclient.save_data(
         obj=train, location=output_train_filepath, save_config=None
     )

modules/ml-pipeline/src/pipeline/4_generate_metrics.py

@@ -4,7 +4,9 @@ After the model is built, we can evaluate its performance
 """
 
 import os
+import yaml
 import pandas as pd
+from pathlib import Path
 from core.interface.InterfaceModels import MLModel
 from core.interface.InterfaceMetrics import MLMetrics
 from core.interface.InterfaceDataClient import DataClient

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

@@ -99,12 +99,6 @@ def generate_scenario_predictions(
             ]
         )
 
-    # TEMPORARY FIX: ADD is_post_sap10_starting and is_post_sap10_ending if not present
-    if "is_post_sap10_starting" not in scenario_data.columns:
-        scenario_data["is_post_sap10_starting"] = False
-    if "is_post_sap10_ending" not in scenario_data.columns:
-        scenario_data["is_post_sap10_ending"] = False
-
     logger.info("--- Loading Model ---")
 
     model.load_model(model_filepath)

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

@@ -14,23 +14,9 @@ default:
       output_filepath: ./data/model/allmodels/
       problem_type: regression
       eval_metric: mean_squared_error #mean_absolute_error
-      time_limit: 3600
+      time_limit: 1800
       presets: medium_quality
-      excluded_model_types: ['RF', 'CAT', 'NN_TORCH', 'KNN', 'XT', 'FASTAI']
-      infer_limit: 1
+      excluded_model_types: ['RF', 'CAT', 'NN_TORCH', 'KNN', 'XT']
+      infer_limit: 0.05
       infer_limit_batch_size: 10000
-      fit_strategy: "parallel"
       ag_args_ensemble: {'num_folds_parallel': 2}
-      num_gpus: 0
-      hyperparameters:
-        {
-        'NN_TORCH': [{}],
-        'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0,}}],
-        # 'GBM': [{}],
-        'CAT': [{}],
-        'XGB': [{}],
-        'FASTAI': [{}],
-        'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
-        'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
-        'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
-        }

modules/ml-pipeline/src/pipeline/configs/feature_processor_logic.py

@@ -18,60 +18,30 @@ def remove_starting_columns(df):
     return df
 
 
-def keep_negative_heat_change(df):
-    df = df[df["heat_demand_change"] < 0]
+def remove_floor_height_ending(df):
+    # df.describe(percentiles=[0.005,0.99])['FLOOR_HEIGHT_ENDING']
+    # shows bottom 0.5 percentile is 1.665
+    # So keep anything above this
+    df = df[df["floor_height_ending"] > 1.665].reset_index(drop=True)
+    print("we in here")
     return df
 
 
-def keep_negative_carbon_change(df):
-    df = df[df["carbon_change"] < 0]
+def remove_minimum_habitable_room_size(df):
+    # Need minimum of 6.5m per habitable room
+    df = df[
+        df["total_floor_area_ending"] / df["number_habitable_rooms"] > 6.5
+    ].reset_index(drop=True)
     return df
 
 
-# TODO: Move to ETL pipeline
-def remove_unreasonable_habitable_rooms(df):
-    """
-    Assumption is that proportion of floor area to habitable rooms should be at least 6.5m2
-    """
-    minimum_room_size_index = (
-        df["total_floor_area_ending"] / df["number_habitable_rooms"] >= 6.5
-    )
-    df = df[minimum_room_size_index]
+def keep_flats(df):
+    df = df[df["property_type"] == "Flat"]
     return df
 
 
-def remove_top_1_percent_heat_demand_starting(df):
-    # threshold_value = df.describe(percentiles=[0.99])['HEAT_DEMAND_STARTING']['99%']
-    threshold_value = 860
-    df = df[df["heat_demand_starting"] < threshold_value]
-    return df
-
-
-def remove_negative_heat_demand_starting(df):
-    # threshold_value = df.describe(percentiles=[0.99])['HEAT_DEMAND_STARTING']['99%']
-    threshold_value = 0
-    df = df[df["heat_demand_starting"] > threshold_value]
-    return df
-
-
-# def remove_top_1_percent_heat_demand_ending(df):
-#     # threshold_value = df.describe(percentiles=[0.99])['HEAT_DEMAND_STARTING']['99%']
-#     threshold_value = 593
-#     df = df[df["heat_demand_ending"] < threshold_value]
-#     return df
-
-
-def remove_negative_heat_demand_ending(df):
-    # threshold_value = df.describe(percentiles=[0.99])['HEAT_DEMAND_STARTING']['99%']
-    threshold_value = 0
-    df = df[df["heat_demand_ending"] > threshold_value]
-    return df
-
-
-def remove_top_1_percent_carbon(df):
-    # threshold_value = df.describe(percentiles=[0.99])['CARBON_STARTING']['99%']
-    threshold_value = 18
-    df = df[df["carbon_starting"] < threshold_value]
+def keep_non_zero_rdsap(df):
+    df = df[df["rdsap_change"] != 0]
     return df
 
 
@@ -84,14 +54,10 @@ def remove_top_1_percent_carbon(df):
 #     return df
 
 business_logic = {
-    "remove_unreasonable_habitable_rooms": remove_unreasonable_habitable_rooms,
-    "keep_negative_heat_change": keep_negative_heat_change,
-    "keep_negative_carbon_change": keep_negative_carbon_change,
-    "remove_top_1_percent_heat_demand": remove_top_1_percent_heat_demand_starting,
-    "remove_negative_heat_demand_starting": remove_negative_heat_demand_starting,
-    # "remove_top_1_percent_heat_demand_ending": remove_top_1_percent_heat_demand_ending,
-    "remove_negative_heat_demand_ending": remove_negative_heat_demand_ending,
-    "remove_top_1_percent_carbon": remove_top_1_percent_carbon,
+    # "keep_non_zero_rdsap": keep_non_zero_rdsap,
+    # "keep_flats": keep_flats,
+    # "remove_minimum_habitable_room_size": remove_minimum_habitable_room_size,
+    # "remove_floor_height_ending": remove_floor_height_ending
     # "remove_starting_columns": remove_starting_columns
     # "keep_ENDING_COLUMNS": keep_ending_columns
 }

modules/ml-pipeline/src/pipeline/configs/post_prediction_logic.py

@@ -1,7 +1,6 @@
 """
 After predictions, we may want to apply some post processing to the predictions
 """
-
 import pandas as pd
 
 
@@ -14,11 +13,10 @@ def clip_predictions_to_minimum_value(
     predictions_df = pd.concat([data, predictions], axis=1)
     # We expect all prediction to be atleast one point improvement
     replace_index = (
-        predictions_df["predictions"]
-        > predictions_df["heat_demand_starting"] - minimum_value
+        predictions_df["sap_starting"] + minimum_value > predictions_df["predictions"]
     )
     predictions_df.loc[replace_index, "predictions"] = (
-        predictions_df.loc[replace_index, "heat_demand_starting"] - minimum_value
+        predictions_df.loc[replace_index, "sap_starting"] + minimum_value
     )
 
     predictions_new = predictions_df["predictions"]

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

@@ -8,6 +8,6 @@ default:
       # - 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
+      - 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

@@ -18,29 +18,26 @@ default:
   prepare_data:
     input_dataclient_type: aws-s3
     output_dataclient_type: local
-    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-06-09-10-36-53/dataset_rooms.parquet
-    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-10-03-22-57-23/dataset_rooms.parquet
-    data_filepath: s3://retrofit-data-dev/sap_change_model/2025-11-02-09-32-42/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
-    sample_test_filepath: ./data/prepared_data/sample_test.parquet
-
 
   feature_processor:
     feature_processor_type: dataframe
     feature_processor_config:
       subsample_amount: null
       subsample_seed: 0
-      target: heat_demand_ending
+      target: sap_ending
       identifier_columns: ["uprn"]
+      # drop_columns: ["heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", "days_to_starting", "days_to_ending"]
       drop_columns: [
-        "heat_demand_change", "carbon_change", "rdsap_change", "sap_ending", "carbon_ending", "days_to_starting", "days_to_ending",
+        "heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", "days_to_starting", "days_to_ending",
         'number_habitable_rooms_starting', 'number_habitable_rooms_ending', 'number_heated_rooms_starting', 'number_heated_rooms_ending',
-        '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',
-        'floor_thermal_transmittance', 'floor_thermal_transmittance_ending', 'lodgement_date_starting', 'lodgement_date_ending',]
-      # retain_features: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"]
+        'number_habitable_rooms', 'number_heated_rooms']
       retain_features: null
       # retain_features: ['uprn', 'sap_starting', 'hot_water_energy_eff_ending',
       #  'mainheat_energy_eff_ending', 'constituency', 'roof_energy_eff_ending',

modules/ml-pipeline/src/pipeline/core/MLModels.py

@@ -1,4 +1,4 @@
-""" "
+""""
 Implementations of MLModels, all of which will have four methods to:
 - Load model
 - Save Model
@@ -11,6 +11,9 @@ import joblib
 import pandas as pd
 from pathlib import Path
 from typing import Union, List
+from sklearn import linear_model
+from sklearn.svm import SVR
+from autogluon.tabular import TabularDataset, TabularPredictor
 from core.interface.InterfaceModels import MLModel
 from core.Logger import logger
 
@@ -66,8 +69,6 @@ class SKLearnLinearRegression:
         """
         Method to train a model
         """
-        from sklearn import linear_model
-
         self.model = linear_model.LinearRegression()
 
         x_train = data.iloc[:, data.columns != target]
@@ -116,7 +117,6 @@ class SKLearnSVMRegression:
         """
         Method to train a model
         """
-        from sklearn.svm import SVR
 
         validate_dict_keys(
             list(model_hyperparameters.keys()),
@@ -152,17 +152,12 @@ class AutogluonAutoML:
         "infer_limit",
         "infer_limit_batch_size",
         "ag_args_ensemble",
-        "fit_strategy",
-        "num_gpus",
-        "hyperparameters",
     ]
 
     def load_model(self, path: Union[Path, str]) -> None:
         """
         Method to load a model
         """
-        from autogluon.tabular import TabularPredictor
-
         filepath = str(path)
         self.model = TabularPredictor.load(path=filepath)
 
@@ -188,10 +183,6 @@ class AutogluonAutoML:
         """
         Method to train a model
         """
-        from autogluon.tabular import TabularDataset, TabularPredictor
-
-        # Force Parallel Model fitting
-        os.environ["AG_FORCE_PARALLEL"] = "True"
 
         validate_dict_keys(
             keys_1=list(model_hyperparameters.keys()),
@@ -218,9 +209,6 @@ class AutogluonAutoML:
             infer_limit=model_hyperparameters["infer_limit"],
             infer_limit_batch_size=model_hyperparameters["infer_limit_batch_size"],
             ag_args_ensemble=model_hyperparameters["ag_args_ensemble"],
-            fit_strategy=model_hyperparameters["fit_strategy"],
-            num_gpus=model_hyperparameters["num_gpus"],
-            hyperparameters=model_hyperparameters["hyperparameters"].to_dict(),
         )
 
     def predict(

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

@@ -16,15 +16,15 @@ stages:
     deps:
     - path: 1_prepare_data.py
       hash: md5
-      md5: a5ce162e1c402c0f811a80ef78cf4dd5
-      size: 4481
+      md5: 11a3b8bfdfe199ab7ecc39ccc5652649
+      size: 4298
     params:
       configs/settings.yaml:
         default.feature_processor.feature_processor_config.drop_columns:
         - heat_demand_change
         - carbon_change
         - rdsap_change
-        - sap_ending
+        - heat_demand_ending
         - carbon_ending
         - days_to_starting
         - days_to_ending
@@ -34,37 +34,24 @@ stages:
         - number_heated_rooms_ending
         - 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
-        - floor_thermal_transmittance
-        - floor_thermal_transmittance_ending
-        - lodgement_date_starting
-        - lodgement_date_ending
         default.feature_processor.feature_processor_config.retain_features:
         default.feature_processor.feature_processor_config.subsample_amount:
         default.feature_processor.feature_processor_config.subsample_seed: 0
-        default.feature_processor.feature_processor_config.target: 
-          heat_demand_ending
+        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/2025-11-02-09-32-42/dataset_rooms.parquet
+        default.prepare_data.data_filepath:
+          s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/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
-        default.prepare_data.output_train_filepath: 
-          ./data/prepared_data/train.parquet
+        default.prepare_data.output_test_filepath: ./data/prepared_data/test.parquet
+        default.prepare_data.output_train_filepath: ./data/prepared_data/train.parquet
         default.prepare_data.train_proportion: 0.9
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: e3bfd536e80a5e0289eb72d424b621d4.dir
-      size: 37960889
-      nfiles: 3
+      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      size: 45056059
+      nfiles: 2
   build_model:
     cmd: python 2_build_model.py
     deps:
@@ -74,9 +61,9 @@ stages:
       size: 4820
     - path: data/prepared_data
       hash: md5
-      md5: e3bfd536e80a5e0289eb72d424b621d4.dir
-      size: 37960889
-      nfiles: 3
+      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      size: 45056059
+      nfiles: 2
     params:
       configs/build_model.yaml:
         default:
@@ -92,7 +79,7 @@ stages:
               output_filepath: ./data/model/allmodels/
               problem_type: regression
               eval_metric: mean_squared_error
-              time_limit: 3600
+              time_limit: 1800
               presets: medium_quality
               excluded_model_types:
               - RF
@@ -100,94 +87,25 @@ stages:
               - NN_TORCH
               - KNN
               - XT
-              - FASTAI
-              infer_limit: 1
+              infer_limit: 0.05
               infer_limit_batch_size: 10000
-              fit_strategy: parallel
               ag_args_ensemble:
                 num_folds_parallel: 2
-              num_gpus: 0
-              hyperparameters:
-                NN_TORCH:
-                - {}
-                GBM:
-                - extra_trees: true
-                  ag_args:
-                    name_suffix: XT
-                - {}
-                - learning_rate: 0.03
-                  num_leaves: 128
-                  feature_fraction: 0.9
-                  min_data_in_leaf: 3
-                  ag_args:
-                    name_suffix: Large
-                    priority: 0
-                CAT:
-                - {}
-                XGB:
-                - {}
-                FASTAI:
-                - {}
-                RF:
-                - criterion: gini
-                  ag_args:
-                    name_suffix: Gini
-                    problem_types:
-                    - binary
-                    - multiclass
-                - criterion: entropy
-                  ag_args:
-                    name_suffix: Entr
-                    problem_types:
-                    - binary
-                    - multiclass
-                - criterion: squared_error
-                  ag_args:
-                    name_suffix: MSE
-                    problem_types:
-                    - regression
-                    - quantile
-                XT:
-                - criterion: gini
-                  ag_args:
-                    name_suffix: Gini
-                    problem_types:
-                    - binary
-                    - multiclass
-                - criterion: entropy
-                  ag_args:
-                    name_suffix: Entr
-                    problem_types:
-                    - binary
-                    - multiclass
-                - criterion: squared_error
-                  ag_args:
-                    name_suffix: MSE
-                    problem_types:
-                    - regression
-                    - quantile
-                KNN:
-                - weights: uniform
-                  ag_args:
-                    name_suffix: Unif
-                - weights: distance
-                  ag_args:
-                    name_suffix: Dist
     outs:
     - path: data/fit_predictions/
       hash: md5
-      md5: 203a83038aa79f61feaa8e7b036ec12c.dir
-      size: 3008451
+      md5: d9c9afc05e8780db47c0548b19bf7d19.dir
+      size: 3349989
       nfiles: 1
     - path: data/model/
       hash: md5
-      md5: 42b9588e11bbe599aea65e7560f1d217.dir
-      size: 780134010
-      nfiles: 32
+      md5: 13c3100e1486c27a83a8a47491077842.dir
+      size: 773523079
+      nfiles: 36
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: da7294ddfffd1a3613a731ff2685d814
-      size: 221
+      md5: 2ff70a2a45813e1bcdf2ea3aa8e07d4a
+      size: 224
   generate_predictions:
     cmd: python 3_generate_predictions.py
     deps:
@@ -197,46 +115,44 @@ stages:
       size: 2464
     - path: data/model
       hash: md5
-      md5: 42b9588e11bbe599aea65e7560f1d217.dir
-      size: 780134010
-      nfiles: 32
+      md5: 13c3100e1486c27a83a8a47491077842.dir
+      size: 773523079
+      nfiles: 36
     - path: data/prepared_data
       hash: md5
-      md5: e3bfd536e80a5e0289eb72d424b621d4.dir
-      size: 37960889
-      nfiles: 3
+      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      size: 45056059
+      nfiles: 2
     params:
       configs/settings.yaml:
         default.generate_predictions.input_dataclient_type: local
         default.generate_predictions.output_dataclient_type: local
         default.generate_predictions.predictions_column_name: predictions
-        default.generate_predictions.predictions_output_filepath: 
-          ./data/predictions/predictions.parquet
-        default.generate_predictions.test_data_filepath: 
-          ./data/prepared_data/test.parquet
+        default.generate_predictions.predictions_output_filepath: ./data/predictions/predictions.parquet
+        default.generate_predictions.test_data_filepath: ./data/prepared_data/test.parquet
     outs:
     - path: data/predictions/
       hash: md5
-      md5: 256e8a11b0d6ab414f97b89d4658dea3.dir
-      size: 406659
+      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
+      size: 463197
       nfiles: 1
   generate_metrics:
     cmd: python 4_generate_metrics.py
     deps:
     - path: 4_generate_metrics.py
       hash: md5
-      md5: d61bb524f706917f6a3eb72b1ab8bc61
-      size: 3447
+      md5: 4fedb86d89d528f0a6597934ba3890a0
+      size: 3484
     - path: data/predictions
       hash: md5
-      md5: 256e8a11b0d6ab414f97b89d4658dea3.dir
-      size: 406659
+      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
+      size: 463197
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: e3bfd536e80a5e0289eb72d424b621d4.dir
-      size: 37960889
-      nfiles: 3
+      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      size: 45056059
+      nfiles: 2
     params:
       configs/settings.yaml:
         default.generate_metrics.dataclient_type: local
@@ -245,29 +161,30 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: 559d598d06a40bcc337e9f9bf1c45edf
-      size: 221
+      md5: 3e08df02fd5c5d094bcf936e1338d596
+      size: 223
   generate_scenerio_metrics:
     cmd: python 5_generate_scenarios.py
     deps:
     - path: 5_generate_scenarios.py
       hash: md5
-      md5: 872b0c762ce1c8933fcbc5f54d5d4b5d
-      size: 5658
+      md5: 40506749fefd926d47c60ff5b16db307
+      size: 5337
     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
           comparison_output_filepath: ./metrics/scenario_table.md
           metrics_output_filepath: ./metrics/scenario_metrics.md
     outs:
     - path: metrics/scenario_metrics.md
       hash: md5
-      md5: d41d8cd98f00b204e9800998ecf8427e
-      size: 0
+      md5: fa4d6d7bbd7818613800da5f8f37ea96
+      size: 363
     - path: metrics/scenario_table.md
       hash: md5
-      md5: d41d8cd98f00b204e9800998ecf8427e
-      size: 0
+      md5: d6baf100a1623cc2467c2f8221d314c9
+      size: 2133

modules/ml-pipeline/src/pipeline/eda.py

@@ -1,7 +1,6 @@
 """
 Doing some eda on dataset
 """
-
 # Look at response variable
 
 from matplotlib import pyplot as plt
@@ -39,6 +38,7 @@ train_df[[target, "SAP_STARTING"]].plot(y=target, x="SAP_STARTING", style="o")
 train_df[[target, "HEAT_DEMAND_STARTING"]].plot(
     x=target, y="HEAT_DEMAND_STARTING", style="o"
 )
+
 # Both make sense: i.e. the higher the sap, the lower we predict and the higher the heat demand, the higher we predict
 
 # Load the autogluon model and check feature importance
@@ -176,8 +176,6 @@ plot_permutation_importance(exp, fig_kw={"figwidth": 7, "figheight": 6})
 #
 #
 
-from core.MLMetrics import metrics_factory
-
 from core.MLModels import model_factory
 from core.DataClient import dataclient_factory
 import pandas as pd
@@ -218,12 +216,6 @@ mix_df["residual"] = abs(mix_df[predictions_column_name] - mix_df[target])
 mix_df = mix_df.sort_values("residual", ascending=False)
 
 cosine_similarity_df = mix_df[mix_df.columns.difference(["predictions", "residual"])]
-metrics = metrics_factory("Regression")
-metrics.generate_metrics(mix_df["predictions"], mix_df["HEAT_DEMAND_ENDING"])
-
-cosine_similarity_df = mix_df[
-    mix_df.columns.difference(["predictions", "residual", "SAP_ENDING"])
-]
 from sklearn.metrics.pairwise import cosine_similarity
 
 row_index = 0

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

@@ -1,7 +1,7 @@
-joblib==1.5.2
-boto3==1.40.61
-pandas==2.3.3
-autogluon.tabular[all]==1.4.0
-dynaconf==3.2.12
-pyarrow==20.0.0
-pre-commit==4.3.0
+joblib==1.3.2
+boto3==1.28.17
+pandas==2.1.4
+autogluon.tabular[all]==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==1.5.2
-boto3==1.40.61
-pandas==2.3.3
-autogluon.tabular[all]==1.4.0
-dynaconf==3.2.12
-pyarrow==20.0.0
-PyYAML==6.0.3
+joblib==1.3.2
+boto3==1.28.17
+pandas==2.1.4
+autogluon.tabular[all]==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

@@ -1,10 +1,10 @@
-joblib==1.5.2
-boto3==1.40.61
-pandas==2.3.3
-autogluon.tabular[all]==1.4.0
-ray==2.44.1
-dynaconf==3.2.12
-# alibi
-shap==0.49.1
-pyarrow==20.0.0
-pre-commit==4.3.0
+joblib==1.3.2
+boto3==1.28.17
+pandas==2.1.4
+autogluon.tabular[all]==1.0.0
+ray==2.6.3
+dynaconf==3.2.1
+alibi==0.9.5
+shap==0.42.1
+pyarrow==13.0.0
+pre-commit==3.3.3

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

@@ -1,4 +1,4 @@
-boto3==1.40.61
-pandas==2.3.3
-autogluon.tabular[all]==1.4.0
-dynaconf==3.2.12
+boto3==1.28.41
+pandas==2.1.4
+autogluon.tabular[all]==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-s3==3.2.0
-gto==1.9.0
+gto==1.7.1
 pyOpenSSL==23.3.0