Merge pull request #164 from Hestia-Homes/heatingkwh-dev-update

general improvements as per sap model

.github/workflows/Deploy.yml

@@ -2,7 +2,7 @@ name: Sap Change Model Deploy
 
 on:
   push:
-    branches: [ sap-dev, sap-prod, heat-dev, heat-prod, carbon-dev, carbon-prod]
+    branches: [ sap-dev, sap-prod, heat-dev, heat-prod, carbon-dev, carbon-prod, heatingkwh-dev, heatingkwh-prod]
 
 jobs:
   deploy:

.github/workflows/MLPipelinePostMerge.yml

@@ -13,6 +13,7 @@ on:
       - "sap-dev"
       - "heat-dev"
       - "carbon-dev"
+      - "heatingkwh-dev"
 
 permissions: write-all
 

.github/workflows/MLPipelinePullRequest.yml

@@ -5,7 +5,7 @@ on:
   #   branches:
   #     - "model-**"
   pull_request:
-    branches: ["sap-dev", "heat-dev", "carbon-dev"]
+    branches: ["sap-dev", "heat-dev", "carbon-dev", "heatingkwh-dev"]
   label:
     types: ["created", "edited"]
 
@@ -31,6 +31,80 @@ jobs:
   #     run: |
   #       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:
 

MODEL_REGISTRY.md

@@ -16,17 +16,57 @@
         "active": true
     },
     "heat": {
-        "version": "v0.5.0",
+        "version": "v0.6.0",
         "stage": {
-            "dev": "v0.5.0"
+            "dev": "v0.6.0"
         },
         "registered": true,
         "active": true
     },
     "carbon": {
-        "version": "v0.5.0",
+        "version": "v0.6.0",
         "stage": {
-            "dev": "v0.5.0"
+            "dev": "v0.6.0"
+        },
+        "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": "v1.2.0",
+        "stage": {
+            "dev": "v1.2.0"
+        },
+        "registered": true,
+        "active": true
+    },
+    "heatingkwh": {
+        "version": "v1.5.0",
+        "stage": {
+            "dev": "v1.5.0"
         },
         "registered": true,
         "active": true

README.md

@@ -83,3 +83,13 @@ 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

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

@@ -47,6 +47,30 @@ 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
@@ -66,9 +90,6 @@ 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
@@ -78,6 +99,32 @@ 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",
@@ -95,7 +142,7 @@ def handler(event, context):
             output_dataclient=output_dataclient,
             model=model,
             target=feature_process_params["feature_processor_config"]["target"],
-            model_filepath=build_model_params["model_save_filepath"],
+            model_filepath=model_filepath,
             test_data_filepath=body["file_location"],
             predictions_output_filepath=storage_filepath,
             predictions_column_name=generate_predictions_params[

deployment/serverless.yml

@@ -51,3 +51,4 @@ 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

@@ -1,7 +1,8 @@
 export PYENV_ROOT=$(HOME)/.pyenv
 export PATH := $(PYENV_ROOT)/bin:$(PATH)
-PYTHON_VERSION ?= 3.10.12
+PYTHON_VERSION ?= 3.12.12
 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
@@ -12,11 +13,15 @@ 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 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
+	${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
 	echo "TO ACTIVATE ENVIRONMENT, USE THE FOLLOWING COMMAND"
 	echo "conda activate ${CONDA_ENV}"
 
@@ -33,4 +38,4 @@ 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/README.MD

@@ -17,14 +17,15 @@ Within `src` folder, the structure is as follows:
 
 # How to develop using this pipeline:
 
-Run `make init`, which will:
-- Download pyenv (Python version management)
-- Download Python 3.X.X as defined in the `make` file - current 3.10.12
-- Create a virtual environment with this version of python
+First, download miniconda to use conda to manage Python Environments
+Rund `conda init`, to initialise your terminal
+
+Change to this directory and run `make init`, which will:
+- Create a conda virtual environment with this version of python - current 3.10.12
 - Install packages in the training and version control directories in the pipeline folder (dev version if applicable)
 - Install pre-commit to enable pre-commit hooks
 
-To use the environment, run `source .dev_env_pipeline/bin/activate`.
+To use the environment, run `conda activate dev_env_pipeline`
 
 To enable the virtual envrionemnt created in vscode:
 - Open settings

modules/ml-pipeline/src/Prediction.Dockerfile

@@ -1,16 +1,21 @@
 # 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
+FROM python:3.12.12-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 -r requirements.txt
+
+RUN pip install uv
+
+RUN uv pip install -r requirements.txt --system
+
+# 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

@@ -29,6 +29,7 @@ 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 ---")
@@ -99,6 +100,10 @@ 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/5_generate_scenarios.py

@@ -99,6 +99,12 @@ 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

@@ -14,9 +14,23 @@ default:
       output_filepath: ./data/model/allmodels/
       problem_type: regression
       eval_metric: mean_squared_error #mean_absolute_error
-      time_limit: 1800
+      time_limit: 3600
       presets: medium_quality
-      excluded_model_types: ['RF', 'CAT', 'NN_TORCH', 'KNN', 'XT']
-      infer_limit: 0.05
+      excluded_model_types: ['RF', 'CAT', 'NN_TORCH', 'KNN', 'XT', 'FASTAI']
+      infer_limit: 1
       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': [{}, {'max_depth': 10, 'ag_args': {'name_suffix': 'Deep'}}],
+        '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

@@ -5,6 +5,18 @@ During the feature processor step, we can apply additional business logic and fe
 """
 Business Logic dict + functions
 """
+import pandas as pd
+import numpy as np
+import boto3
+import msgpack
+
+s3 = boto3.resource('s3')
+
+# Get the MessagePack data from S3
+obj = s3.Object("retrofit-data-dev", "cleaned_epc_data/cleaned.bson")
+cleaned = obj.get()['Body'].read()
+
+cleaned = msgpack.unpackb(cleaned, raw=False)
 
 
 def remove_starting_columns(df):
@@ -44,6 +56,111 @@ def keep_non_zero_rdsap(df):
     df = df[df["rdsap_change"] != 0]
     return df
 
+def remove_heatingkwh_bottom_percentile(df, percentile=0.0001):
+    df = df[df["heating_kwh"] > df["heating_kwh"].quantile(percentile)]
+    return df
+
+def add_features_from_code(df):
+
+    FEATURES = {
+        "heating_kwh": [
+            "lodgement-year", "lodgement-month", "current-energy-efficiency", "energy-consumption-current",
+            "heating-cost-current", "heating-cost-potential", "total-floor-area", "number-heated-rooms",
+            "mainheat-description", "mainheat-energy-eff", "main-fuel", "secondheat-description", "property-type",
+            "built-form", "mainheatcont-description", "hotwater-description", "hot-water-energy-eff",
+            "walls-energy-eff",
+            "roof-energy-eff", "windows-description", "windows-energy-eff", "floor-description", "flat-top-storey",
+            "flat-storey-count", "unheated-corridor-length", "solar-water-heating-flag", "mechanical-ventilation",
+            "low-energy-lighting", "environment-impact-current", "energy-tariff",
+            "county", "construction-age-band", "co2-emissions-current",
+        ],
+        "hot_water_kwh": [
+            "lodgement-year", "lodgement-month",
+            "current-energy-efficiency",
+            "energy-consumption-current",
+            "hot-water-cost-current",
+            "total-floor-area", "number-heated-rooms",
+            "hotwater-description", "hot-water-energy-eff", "main-fuel", "property-type", "built-form",
+            "co2-emissions-current",
+        ]
+    }
+    CATEGORICAL_COLUMNS = [
+        "lodgement-year", "lodgement-month", "main-fuel", "mainheat-description", "number-heated-rooms",
+        "number-habitable-rooms", "mainheat-energy-eff", "mainheatcont-description", "property-type", "built-form",
+        "construction-age-band", "secondheat-description", "hotwater-description", "hot-water-energy-eff",
+        "walls-description", "walls-energy-eff", "roof-description", "roof-energy-eff", "floor-description",
+        "county",
+        "windows-description", "windows-energy-eff", "flat-top-storey",
+        "flat-storey-count", "unheated-corridor-length", "solar-water-heating-flag", "mechanical-ventilation",
+        "low-energy-lighting", "environment-impact-current", "energy-tariff", "current-energy-rating"
+    ]
+
+    NUMERICAL_COLUMNS = list({
+            x for x in FEATURES["heating_kwh"] + FEATURES["hot_water_kwh"]
+            if x not in CATEGORICAL_COLUMNS
+        })
+    
+
+    """Performs feature engineering on the dataset."""
+    df["lodgement-date"] = pd.to_datetime(df["lodgement-date"])
+    df["lodgement-year"] = df["lodgement-date"].dt.year
+    df["lodgement-month"] = df["lodgement-date"].dt.month
+
+    # For walls, roof, floor description where we have average thermal transmittance, to avoid too many categories
+    # we group them
+    ranges = {
+        "lessthan 0.1": (0, 0.1),
+        "0.1 - 0.3": (0.1, 0.3),
+        "0.3 - 0.5": (0.3, 0.5),
+        "morethan 0.5": (0.5, 2.5),
+    }
+
+    # Generate the lookup table
+    thermal_transmittance_lookup_table = []
+    for i in range(1, 251):
+        value = i / 100
+        for label, (low, high) in ranges.items():
+            if low < value <= high:
+                thermal_transmittance_lookup_table.append({"from": value, "to": label})
+                break
+
+    # Convert to DataFrame for display
+    thermal_transmittance_lookup_table = pd.DataFrame(thermal_transmittance_lookup_table)
+    thermal_transmittance_lookup_table["from"] = thermal_transmittance_lookup_table["from"].astype(str)
+
+    # Apply the lookup table to the data
+    for feature in ["walls-description", "roof-description", "floor-description"]:
+        cleaned_df = pd.DataFrame(cleaned[feature])[["original_description", "thermal_transmittance"]]
+        # Round to 2 decimal places and convert to string
+        cleaned_df["thermal_transmittance"] = cleaned_df["thermal_transmittance"].round(2).astype(str)
+
+        df = df.merge(
+            cleaned_df,
+            how="left",
+            left_on=feature,
+            right_on="original_description",
+        )
+        # We now have the thermal transmittance in the data, which we can use to group with the lookup table
+        df = df.merge(
+            thermal_transmittance_lookup_table,
+            how="left",
+            left_on="thermal_transmittance",
+            right_on="from",
+        )
+        # Where "to" is populated, replace feature with to
+        df[feature] = np.where(
+            ~pd.isnull(df["to"]),
+            df["to"],
+            df[feature]
+        )
+        df = df.drop(columns=["original_description", "thermal_transmittance", "from", "to"])
+
+    # Convert data types
+    df[NUMERICAL_COLUMNS] = df[NUMERICAL_COLUMNS].apply(pd.to_numeric)
+    df[CATEGORICAL_COLUMNS] = df[CATEGORICAL_COLUMNS].astype(str)
+
+    return df
+
 
 # def keep_ending_columns(df):
 #     ending_column_index = [ col_name.endswith("_ENDING") for col_name in list(df.columns)]
@@ -53,7 +170,42 @@ def keep_non_zero_rdsap(df):
 #     df = df[keep_columns]
 #     return df
 
+def enforce_minimum_habitable_room_size(df):
+    # Need minimum of 6.5m per habitable room
+    df = df[
+        df["total-floor-area"] / df["number-habitable-rooms"].astype(float) > 6.5
+    ].reset_index(drop=True)
+    return df
+
+def round_to_100s(df):
+    df['heating_kwh'] = (df['heating_kwh']/100).round()*100
+    return df 
+
+def remove_high_ratio_of_area_to_rooms(df):
+    df['area-to-heated-rooms'] = df['total-floor-area'] / df['number-heated-rooms'].astype(float)
+
+    # Remove na rows
+    df = df[(df['area-to-heated-rooms'].notna())].reset_index(drop=True)
+
+    # change any infinite values to 0
+    df['area-to-heated-rooms'] = df['area-to-heated-rooms'].replace([np.inf], 0)
+
+    # Remove top 0.05% of area-to-heated-rooms
+    df = df[df['area-to-heated-rooms'] < df['area-to-heated-rooms'].quantile(0.9995)].reset_index(drop=True)
+    df = df.drop(columns=['area-to-heated-rooms'])
+    return df
+
+def add_estimate_annual_kwh(df):
+    df['estimate_annual_kwh'] = df['energy-consumption-current'] * df['total-floor-area']
+    return df
+
 business_logic = {
+    "add_features_from_code": add_features_from_code,
+    "remove_heatingkwh_bottom_percentile": remove_heatingkwh_bottom_percentile,
+    # "round_to_100s": round_to_100s,
+    "enforce_minimum_habitable_room_size": enforce_minimum_habitable_room_size,
+    "remove_high_ratio_of_area_to_rooms": remove_high_ratio_of_area_to_rooms,
+    "add_estimate_annual_kwh": add_estimate_annual_kwh,
     # "keep_non_zero_rdsap": keep_non_zero_rdsap,
     # "keep_flats": keep_flats,
     # "remove_minimum_habitable_room_size": remove_minimum_habitable_room_size,

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

@@ -30,6 +30,6 @@ def clip_predictions_to_minimum_value(
 
 
 post_prediction_logic = {
-    "clip_predictions_to_minimum_value": clip_predictions_to_minimum_value,
+    # "clip_predictions_to_minimum_value": clip_predictions_to_minimum_value,
     # "round_predictions": round_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

@@ -21,47 +21,75 @@ default:
     # 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
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-07-03-23-11-39/dataset_rooms.parquet
+    # data_filepath: s3://retrofit-data-dev/energy_consumption/2024-07-08/energy_consumption_dataset.parquet
+    data_filepath: s3://retrofit-data-dev/energy_consumption/2024-07-25/energy_consumption_dataset.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: sap_ending
+      target: heating_kwh
       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", "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']
-      retain_features: null
-      # 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']
+      drop_columns: ["hot_water_kwh"]
+      retain_features: [
+        'uprn', 
+        # 'heating-cost-current',
+        'co2-emissions-current',
+        # 'hot-water-cost-current',
+        'total-floor-area',
+        'secondheat-description',
+        'floor-description',
+        'mainheat-energy-eff',
+        'current-energy-efficiency',
+        'walls-energy-eff',
+        'roof-energy-eff',
+        'property-type',
+        'mainheat-description',
+        'mechanical-ventilation',
+        'floor-level',
+        'built-form',
+        'walls-description',
+        'mainheatcont-description',
+        'roof-description',
+        'energy-consumption-current',
+        'construction-age-band',
+        'hotwater-description',
+        'main-fuel',
+        'hot-water-energy-eff',
+        'co2-emiss-curr-per-floor-area',
+        'windows-energy-eff',
+        'current-energy-rating',
+        'lodgement-year',
+        'extension-count',
+        'number-open-fireplaces',
+        'number-heated-rooms',
+        'windows-description',
+        # 'photo-supply',
+        'heat-loss-corridor',
+        'flat-top-storey',
+        'unheated-corridor-length',
+        'fixed-lighting-outlets-count',
+        'tenure',
+        'multi-glaze-proportion',
+        'solar-water-heating-flag',
+        'energy-tariff',
+        'floor-height',
+        'constituency',
+        'transaction-type',
+        'floor-energy-eff',
+        'lodgement-month',
+        # 'lighting-cost-current',
+        'glazed-area',
+        # 'main-heating-controls',
+        'estimate_annual_kwh',
+        ]
 
   generate_predictions:
     input_dataclient_type: local

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
@@ -11,9 +11,6 @@ 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
 
@@ -69,6 +66,8 @@ class SKLearnLinearRegression:
         """
         Method to train a model
         """
+        from sklearn import linear_model
+
         self.model = linear_model.LinearRegression()
 
         x_train = data.iloc[:, data.columns != target]
@@ -117,6 +116,7 @@ class SKLearnSVMRegression:
         """
         Method to train a model
         """
+        from sklearn.svm import SVR
 
         validate_dict_keys(
             list(model_hyperparameters.keys()),
@@ -152,12 +152,17 @@ 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)
 
@@ -183,6 +188,10 @@ 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()),
@@ -209,6 +218,9 @@ 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

@@ -16,42 +16,77 @@ stages:
     deps:
     - path: 1_prepare_data.py
       hash: md5
-      md5: 11a3b8bfdfe199ab7ecc39ccc5652649
-      size: 4298
+      md5: a5ce162e1c402c0f811a80ef78cf4dd5
+      size: 4481
     params:
       configs/settings.yaml:
         default.feature_processor.feature_processor_config.drop_columns:
-        - 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
+        - hot_water_kwh
         default.feature_processor.feature_processor_config.retain_features:
+        - uprn
+        - co2-emissions-current
+        - total-floor-area
+        - secondheat-description
+        - floor-description
+        - mainheat-energy-eff
+        - current-energy-efficiency
+        - walls-energy-eff
+        - roof-energy-eff
+        - property-type
+        - mainheat-description
+        - mechanical-ventilation
+        - floor-level
+        - built-form
+        - walls-description
+        - mainheatcont-description
+        - roof-description
+        - energy-consumption-current
+        - construction-age-band
+        - hotwater-description
+        - main-fuel
+        - hot-water-energy-eff
+        - co2-emiss-curr-per-floor-area
+        - windows-energy-eff
+        - current-energy-rating
+        - lodgement-year
+        - extension-count
+        - number-open-fireplaces
+        - number-heated-rooms
+        - windows-description
+        - heat-loss-corridor
+        - flat-top-storey
+        - unheated-corridor-length
+        - fixed-lighting-outlets-count
+        - tenure
+        - multi-glaze-proportion
+        - solar-water-heating-flag
+        - energy-tariff
+        - floor-height
+        - constituency
+        - transaction-type
+        - floor-energy-eff
+        - lodgement-month
+        - glazed-area
+        - estimate_annual_kwh
         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_config.target: heating_kwh
         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
+        default.prepare_data.data_filepath: 
+          s3://retrofit-data-dev/energy_consumption/2024-07-25/energy_consumption_dataset.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: 80c9e138146a1d96b9d16091c207e2e8.dir
-      size: 45056059
-      nfiles: 2
+      md5: d74d92498c1641cffe971f6b0634ccb0.dir
+      size: 9623332
+      nfiles: 3
   build_model:
     cmd: python 2_build_model.py
     deps:
@@ -61,9 +96,9 @@ stages:
       size: 4820
     - path: data/prepared_data
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
-      size: 45056059
-      nfiles: 2
+      md5: d74d92498c1641cffe971f6b0634ccb0.dir
+      size: 9623332
+      nfiles: 3
     params:
       configs/build_model.yaml:
         default:
@@ -79,7 +114,7 @@ stages:
               output_filepath: ./data/model/allmodels/
               problem_type: regression
               eval_metric: mean_squared_error
-              time_limit: 1800
+              time_limit: 3600
               presets: medium_quality
               excluded_model_types:
               - RF
@@ -87,25 +122,97 @@ stages:
               - NN_TORCH
               - KNN
               - XT
-              infer_limit: 0.05
+              - FASTAI
+              infer_limit: 1
               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:
+                - {}
+                - max_depth: 10
+                  ag_args:
+                    name_suffix: Deep
+                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: d9c9afc05e8780db47c0548b19bf7d19.dir
-      size: 3349989
+      md5: c9c8140e5a9fe111e5670810a36cd2ef.dir
+      size: 1545780
       nfiles: 1
     - path: data/model/
       hash: md5
-      md5: 13c3100e1486c27a83a8a47491077842.dir
-      size: 773523079
-      nfiles: 36
+      md5: d9f63a57f146409734cd8f84f707b3d9.dir
+      size: 233231379
+      nfiles: 34
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: 2ff70a2a45813e1bcdf2ea3aa8e07d4a
-      size: 224
+      md5: a3d0eefbd5bd873fa0cd42390ac9575a
+      size: 214
   generate_predictions:
     cmd: python 3_generate_predictions.py
     deps:
@@ -115,26 +222,28 @@ stages:
       size: 2464
     - path: data/model
       hash: md5
-      md5: 13c3100e1486c27a83a8a47491077842.dir
-      size: 773523079
-      nfiles: 36
+      md5: d9f63a57f146409734cd8f84f707b3d9.dir
+      size: 233231379
+      nfiles: 34
     - path: data/prepared_data
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
-      size: 45056059
-      nfiles: 2
+      md5: d74d92498c1641cffe971f6b0634ccb0.dir
+      size: 9623332
+      nfiles: 3
     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: 5d07bcebf3160a72bb18dfd79106e85c.dir
-      size: 463197
+      md5: 95172b679bf045e30fde8b6326780e15.dir
+      size: 163474
       nfiles: 1
   generate_metrics:
     cmd: python 4_generate_metrics.py
@@ -145,14 +254,14 @@ stages:
       size: 3484
     - path: data/predictions
       hash: md5
-      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
-      size: 463197
+      md5: 95172b679bf045e30fde8b6326780e15.dir
+      size: 163474
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
-      size: 45056059
-      nfiles: 2
+      md5: d74d92498c1641cffe971f6b0634ccb0.dir
+      size: 9623332
+      nfiles: 3
     params:
       configs/settings.yaml:
         default.generate_metrics.dataclient_type: local
@@ -161,30 +270,29 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: 3e08df02fd5c5d094bcf936e1338d596
-      size: 223
+      md5: c079b41b1a0033b666f27f99be4e12ef
+      size: 212
   generate_scenerio_metrics:
     cmd: python 5_generate_scenarios.py
     deps:
     - path: 5_generate_scenarios.py
       hash: md5
-      md5: 40506749fefd926d47c60ff5b16db307
-      size: 5337
+      md5: 872b0c762ce1c8933fcbc5f54d5d4b5d
+      size: 5658
     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: fa4d6d7bbd7818613800da5f8f37ea96
-      size: 363
+      md5: d41d8cd98f00b204e9800998ecf8427e
+      size: 0
     - path: metrics/scenario_table.md
       hash: md5
-      md5: d6baf100a1623cc2467c2f8221d314c9
-      size: 2133
+      md5: d41d8cd98f00b204e9800998ecf8427e
+      size: 0

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
-dynaconf==3.2.1
-pyarrow==13.0.0
-pre-commit==3.3.3
+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

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
-dynaconf==3.2.1
-pyarrow==13.0.0
-PyYAML==6.0.1
+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

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

@@ -1,10 +1,10 @@
-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
+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

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
-dynaconf==3.2.1
+boto3==1.40.61
+pandas==2.3.3
+autogluon.tabular[all]==1.4.0
+dynaconf==3.2.12

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

@@ -1,4 +1,4 @@
 dvc==3.51.0
 dvc-s3==3.2.0
-gto==1.7.1
+gto==1.9.0
 pyOpenSSL==23.3.0