Merge pull request #117 from Hestia-Homes/sap-dev

Sap dev

.dockerignore

@@ -0,0 +1,9 @@
+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

@@ -0,0 +1,127 @@
+name: Sap Change Model Deploy
+
+on:
+  push:
+    branches: [ sap-dev, sap-prod, heat-dev, heat-prod, carbon-dev, carbon-prod]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.10.12
+
+      - name: Install Serverless and plugins
+        run: |
+          npm install -g serverless@^3.38.0
+          npm install -g serverless-domain-manager@^7.3.8
+
+      - name: Install DVC
+        run: |
+          pip install --upgrade pip
+          pip install -r modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
+
+      # Set up all of the secrets required for the deployment
+      - name: set secret prefix which is used across multiple steps
+        id: secret_prefix
+        run: |
+            # Convert branch name to uppercase and replace hyphens with underscores
+            echo "::set-output name=secret_prefix::$(echo "${{ github.ref_name }}" | tr 'a-z-' 'A-Z_')"
+
+      - name: Set domain name
+        id: set_domain
+        run: echo "::set-output name=domain::${{ secrets[format('{0}_DOMAIN_NAME', steps.secret_prefix.outputs.secret_prefix)] }}"
+
+      - name: Set ECR credentials
+        id: set_ecr_credentials
+        run: |
+          # Fetch the secret using the secret prefix
+          echo "::set-output name=ecr_uri::${{ secrets[format('{0}_ECR_URI', steps.secret_prefix.outputs.secret_prefix)] }}"
+
+      - name: Set S3 buckets
+        id: set_s3_buckets
+        run: |
+          # Fetch the secret using the secret prefix
+          echo "::set-output name=data_bucket::${{ secrets[format('{0}_DATA_BUCKET', steps.secret_prefix.outputs.secret_prefix)] }}"
+          echo "::set-output name=predictions_bucket::${{ secrets[format('{0}_PREDICTIONS_BUCKET', steps.secret_prefix.outputs.secret_prefix)] }}"
+
+      - name: Set stack_name
+        id: set_stack_name
+        run: |
+          # Take branch prefix and add "model" for stack name
+          stack_name=$( echo ${{ github.ref_name }} | awk -F"-" '{print $1}' | sed 's/$/model/g')
+          if [ -z "${stack_name}" ]; then
+            echo "::set-output name=stack_name::"
+          else
+            echo "::set-output name=stack_name::${stack_name}"
+          fi
+
+      - name: Set runtime_environment
+        id: set_runtime_environment
+        run: |
+          # Extract the suffix after the hyphen from the branch name
+          runtime_environment=$(echo "${{ github.ref_name }}" | awk -F'-' '{print $NF}')
+          echo "::set-output name=runtime_environment::$runtime_environment"
+
+      - name: AWS credentials for dev
+        if: ${{ steps.set_runtime_environment.outputs.runtime_environment }} == 'dev'
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.DEV_AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.DEV_AWS_SECRET_ACCESS_KEY }}
+          aws-region: eu-west-2
+
+      - name: AWS credentials for prod
+        if: ${{ steps.set_runtime_environment.outputs.runtime_environment }} == 'prod'
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.PROD_AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.PROD_AWS_SECRET_ACCESS_KEY }}
+          aws-region: eu-west-2
+
+      - name: DVC Pull
+        run: |
+          cd modules/ml-pipeline/src/pipeline
+          dvc pull -r ${{ steps.set_runtime_environment.outputs.runtime_environment }}
+
+      - name: Setup Docker
+        uses: docker/setup-buildx-action@v1
+
+      - name: Login to ECR
+        run: |
+          aws ecr get-login-password --region eu-west-2 | docker login --username AWS --password-stdin ${{ steps.set_ecr_credentials.outputs.ecr_uri }}
+
+      # Building and pushing Docker image with caching
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v3
+        with:
+          context: .
+          file: ./deployment/Dockerfile.prediction.lambda
+          push: true
+          tags: ${{ steps.set_ecr_credentials.outputs.ecr_uri }}:${{ github.sha }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          platforms: linux/amd64
+          provenance: false
+          build-args: |
+            RUNTIME_ENVIRONMENT=${{ steps.set_runtime_environment.outputs.runtime_environment }}
+
+      - name: Deploy to AWS Lambda via Serverless
+        env:
+          RUNTIME_ENVIRONMENT: ${{ steps.set_runtime_environment.outputs.runtime_environment }}
+          PREDICTIONS_BUCKET: ${{ steps.set_s3_buckets.outputs.predictions_bucket }}
+          DATA_BUCKET:  ${{ steps.set_s3_buckets.outputs.data_bucket }}
+          DOMAIN_NAME: ${{ steps.set_domain.outputs.domain }}
+          ECR_URI: ${{ steps.set_ecr_credentials.outputs.ecr_uri }}
+          GITHUB_SHA: ${{ github.sha }}
+          STACK_NAME: ${{ steps.set_stack_name.outputs.stack_name }}
+        run: |
+          # Deploy to AWS Lambda via Serverless
+          cd deployment
+          sls deploy --config serverless.yml --stage ${{ steps.set_runtime_environment.outputs.runtime_environment }} --verbose

.github/workflows/MLPipelineDevPipeline.yml

@@ -0,0 +1,47 @@
+name: Deployment for Dev Model
+
+on:
+  push:
+    tags:
+      - "**model#dev#*"
+
+permissions: write-all
+
+jobs:
+
+  Register-Prediction-Image-Dev:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - name: Install packages to retrieve artifacts
+      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: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
+      run: |
+        cd modules/ml-pipeline/src/pipeline
+        dvc pull -r dev
+
+    - name: Build Prediction docker image (TODO - NEED LAMBDA IMAGE, need to add version from gto registry)
+      run: |
+        cd modules/ml-pipeline/src/
+        REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
+        docker build . --file Prediction.Dockerfile --tag ${REGISTER_MODEL_NAME}
+
+    - name: ECR Login - Dev
+      env:
+        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
+      run: |
+        echo "LOGIN TO ECR"
+
+    - name: Push Prediction image to ECR - Dev
+      env:
+        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
+      run: |
+        echo "PUSH TO ECR"

.github/workflows/MLPipelinePostMerge.yml

@@ -10,7 +10,9 @@ on:
     types:
       - closed
     branches:
-      - "master"
+      - "sap-dev"
+      - "heat-dev"
+      - "carbon-dev"
 
 permissions: write-all
 
@@ -26,7 +28,7 @@ jobs:
     - name: Install packages to register model
       run: |
         pip install --upgrade pip
-        pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
+        pip install -r modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
 
     - name: Register Model
       run: |
@@ -40,7 +42,14 @@ jobs:
         if [ -z "${latest_version}" ]; then
           increment_version="1.0.0"
         else
-          increment_version=$(echo ${latest_version} | awk -F'.' '{OFS="."; $1+=1; print}')
+          increment_version=$(echo ${latest_version} | awk 'BEGIN {
+              FS="\\."   # Set the field separator to a period
+              OFS="."    # Set the output field separator to a period
+          }
+          {
+              major = $1 + 1   # Increment the major version
+              print major, "0", "0"   # Print the new version
+          }')
         fi
 
         new_tag=${REGISTER_MODEL_NAME}@v${increment_version}
@@ -48,7 +57,7 @@ jobs:
         git tag -a ${new_tag} -m "Registering new Major Version"
         git push origin ${new_tag}
 
-        gto show > MODEL_REGISTRY.md
+        gto show --json > MODEL_REGISTRY.md
         git add .
         git commit -m "Update Registry"
         git push
@@ -64,7 +73,7 @@ jobs:
     - name: Install packages to register model
       run: |
         pip install --upgrade pip
-        pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
+        pip install -r modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
 
     - name: Register Model
       run: |
@@ -78,7 +87,14 @@ jobs:
         if [ -z "${latest_version}" ]; then
           increment_version="0.1.0"
         else
-          increment_version=$(echo ${latest_version} | awk 'BEGIN{FS=OFS="."} {$2++; print}')
+          increment_version=$(echo ${latest_version} | awk 'BEGIN {
+              FS="\\."   # Set the field separator to a period
+              OFS="."    # Set the output field separator to a period
+          }
+          {
+              minor = $2 + 1   # Increment the minor version
+              print $1, minor, "0"   # Print the new version
+          }')
         fi
 
         new_tag=${REGISTER_MODEL_NAME}@v${increment_version}
@@ -86,7 +102,7 @@ jobs:
         git tag -a ${new_tag} -m "Registering new Minor Version"
         git push origin ${new_tag}
 
-        gto show > MODEL_REGISTRY.md
+        gto show --json > MODEL_REGISTRY.md
         git add .
         git commit -m "Update Registry"
         git push
@@ -102,7 +118,7 @@ jobs:
     - name: Install packages to register model
       run: |
         pip install --upgrade pip
-        pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
+        pip install -r modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
 
     - name: Register Model
       run: |
@@ -116,7 +132,14 @@ jobs:
         if [ -z "${latest_version}" ]; then
           increment_version="0.0.1"
         else
-          increment_version=$(echo ${latest_version} | awk 'BEGIN{FS=OFS="."} {$3++; print}')
+          increment_version=$(echo ${latest_version} | awk 'BEGIN {
+              FS="\\."   # Set the field separator to a period
+              OFS="."    # Set the output field separator to a period
+          }
+          {
+              patch = $3 + 1   # Increment the patch version
+              print $1, $2, patch   # Print the new version
+          }')
         fi
 
         new_tag=${REGISTER_MODEL_NAME}@v${increment_version}
@@ -124,7 +147,7 @@ jobs:
         git tag -a ${new_tag} -m "Registering new Patch Version"
         git push origin ${new_tag}
 
-        gto show > MODEL_REGISTRY.md
+        gto show --json > MODEL_REGISTRY.md
         git add .
         git commit -m "Update Registry"
         git push
@@ -138,14 +161,14 @@ jobs:
     - name: Install packages to retrieve artifacts
       run: |
         pip install --upgrade pip
-        pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
+        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/src
+        cd modules/ml-pipeline/src/pipeline
         dvc pull -r experiments
 
     - name: Push artifacts to Dev
@@ -153,7 +176,7 @@ jobs:
         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/src
+        cd modules/ml-pipeline/src/pipeline
         dvc push -r dev
 
   Register-New-Model-Dev:
@@ -173,12 +196,11 @@ jobs:
     - name: Install packages to register model
       run: |
         pip install --upgrade pip
-        pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
+        pip install -r modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
 
     - name: Register Model
       env:
-        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
+        TARGET_BRANCH: ${{ github.base_ref }}
-        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
       run: |
 
         REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
@@ -187,60 +209,30 @@ jobs:
         git config user.name "Github-Bot"
         git config user.email "Github-Bot@no-reply.com"
 
-        latest_dev_version=$(gto history temp --asc --plain | awk '{print $NF}' | awk '/dev/' | awk 'END{print}' | awk -F"#" '{print $3}')
+        latest_dev_version=$(gto history ${REGISTER_MODEL_NAME} --asc --plain | awk '{print $NF}' | awk '/dev/' | awk 'END {print}')
         if [ -z "${latest_dev_version}" ]; then
-          increment_version=1
+          increment_version="1"
         else
-          increment_version=${latest_dev_version} | awk '{$1++; print}'
+          increment_version=$(echo ${latest_dev_version} | awk '{print $NF}' | awk -F"#" '{print $3}' | awk '{$1++; print}')
         fi
 
         new_tag=${REGISTER_MODEL_NAME}#dev#${increment_version}
+        latest_version=$(gto show ${REGISTER_MODEL_NAME}@latest --ref | awk -F"@" '{print $2}')
 
-        git pull #Get new model registry md file changes
+        echo ${new_tag}
-        git tag -a ${new_tag} -m "Registering Latest Version to Dev"
+
+        commit_hash=$(gto history ${REGISTER_MODEL_NAME} --asc --plain | awk "/${latest_version}/" | awk '{print $(NF-1)}')
+        git checkout ${commit_hash}
+
+        # git pull #Get new model registry md file changes
+        git tag -a ${new_tag} -m "Assigning stage dev to artifact ${REGISTER_MODEL_NAME} version ${latest_version}"
         git push origin ${new_tag}
 
-        gto show > MODEL_REGISTRY.md
+        git checkout ${TARGET_BRANCH}
+        git fetch --all
+        git pull
+
+        gto show --json > MODEL_REGISTRY.md
         git add .
         git commit -m "Update Registry"
-        git push origin master
+        git push origin ${TARGET_BRANCH}
-
-
-  Register-Prediction-Image-Dev:
-    needs: [Promote-Artefacts-To-Dev, Register-New-Model-Dev]
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v3
-    - name: Install packages to retrieve artifacts
-      run: |
-        pip install --upgrade pip
-        pip install -r modules/ml-pipeline/src/pipeline/src/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: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
-      run: |
-        cd modules/ml-pipeline/src/pipeline/src
-        dvc pull -r dev
-
-    - name: Build Prediction docker image (TODO - NEED LAMBDA IMAGE, need to add version from gto registry)
-      run: |
-        cd modules/ml-pipeline/src/pipeline/
-        REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
-        docker build . --file Prediction.Dockerfile --tag ${REGISTER_MODEL_NAME}
-
-    - name: ECR Login - Dev
-      env:
-        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
-        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
-      run: |
-        echo "LOGIN TO ECR"
-
-    - name: Push Prediction image to ECR - Dev
-      env:
-        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
-        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
-      run: |
-        echo "PUSH TO ECR"

.github/workflows/MLPipelinePullRequest.yml

@@ -5,7 +5,7 @@ on:
   #   branches:
   #     - "model-**"
   pull_request:
-    branches: [ "master" ]
+    branches: ["sap-dev", "heat-dev", "carbon-dev"]
   label:
     types: ["created", "edited"]
 
@@ -44,19 +44,19 @@ jobs:
         AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
       run: |
         pip install --upgrade pip
-        pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
+        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/src
+        cd modules/ml-pipeline/src/pipeline
         dvc pull -r experiments
 
     - name: Build Prediction docker Image
       run: |
-        cd modules/ml-pipeline/src/pipeline/
+        cd modules/ml-pipeline/src/
         docker build . --file Prediction.Dockerfile --tag prediction_test
 
     - name: Run Prediction docker container
@@ -72,14 +72,14 @@ jobs:
     - name: Install packages to retrieve artifacts
       run: |
         pip install --upgrade pip
-        pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
+        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/src
+        cd modules/ml-pipeline/src/pipeline
         dvc pull -r experiments
 
     - uses: actions/setup-python@v4
@@ -89,13 +89,24 @@ jobs:
         AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
         AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
         REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        TARGET_BRANCH: ${{ github.base_ref }}
       run: |
-        cd modules/ml-pipeline/src/pipeline/src
+        cd modules/ml-pipeline/src/pipeline
         echo "## Model metrics" > report.md
 
         # Compare metrics to master
-        git fetch --depth=1 origin master:master
+        git fetch --depth=1 origin ${TARGET_BRANCH}:${TARGET_BRANCH}
-        dvc metrics diff --md --all master >> report.md
+        dvc metrics diff --md --all ${TARGET_BRANCH} >> report.md
+
+        echo "## Scenario comparison" >> report.md
+
+        cat metrics/scenario_table.md >> report.md
+
+        echo "" >> report.md
+
+        echo "## Scenario metrics" >> report.md
+
+        cat metrics/scenario_metrics.md >> report.md
 
         cml comment create report.md
 

.pre-commit-config.yaml

@@ -10,3 +10,10 @@ repos:
     rev: 22.10.0
     hooks:
     -   id: black
+-   repo: local
+    hooks:
+    -   id: dvc-push-experiment
+        name: DVC - Push to experiment to remote location (experiments)
+        entry: bash -c 'cd modules/ml-pipeline/src/pipeline && dvc push -r experiments || echo "Up to date!"'
+        language: system
+        verbose: true

MODEL_REGISTRY.md

@@ -1,7 +1,34 @@
-╒════════╤══════════╤════════╕
+{
-│ name   │ latest   │ #dev   │
+    "model": {
-╞════════╪══════════╪════════╡
+        "version": "v12.10.12",
-│ bob    │ v1.0.0   │ -      │
+        "stage": {
-│ model  │ v6.1.3   │ -      │
+            "dev": "v11.10.12"
-│ temp   │ v0.0.1   │ v0.0.1 │
+        },
-╘════════╧══════════╧════════╛
+        "registered": true,
+        "active": true
+    },
+    "sap": {
+        "version": "v0.14.0",
+        "stage": {
+            "dev": "v0.14.0"
+        },
+        "registered": true,
+        "active": true
+    },
+    "heat": {
+        "version": "v0.5.0",
+        "stage": {
+            "dev": "v0.5.0"
+        },
+        "registered": true,
+        "active": true
+    },
+    "carbon": {
+        "version": "v0.5.0",
+        "stage": {
+            "dev": "v0.5.0"
+        },
+        "registered": true,
+        "active": true
+    }
+}

README.md

@@ -3,9 +3,83 @@
 Creating a ML-toolkit that can be reused:
 
 - ML pipeline:
-	- A dummy pipeline that has data version control, experiment 
+	- A generic pipeline that has data version control, experiment
 tracking and a model registry
 
 - ML monitoring:
 	- A bolt-on service that can implement model monitoring
 
+There are multiple protected branches which adapt the generic pipeline to produce different models:
+- sap-{dev/staging/prod}-**
+- heat-{dev/staging/prod}-**
+- carbon-{dev/staging/prod}-**
+
+These branches will differ by the configuration files that define the data used and the outputs of the ML-pipeline
+- There can be different additional logic for each branch but the pipeline will be the same.
+
+# Deployment
+
+Scripts associated to deployment can be found in the deployment/ folder.
+
+Deployment is automated via Github Actions, where a deployment is triggered by a push to one of the
+protected branch, with one of dev or prod as the suffix, describing the target environment.
+
+The github actions file will build and push a docker image to ECR and then deploy a lambda
+which produces predictions for the relevant model.
+
+In order for this to be set up, some key environment variables needs to be inserted into Github
+secrets. Each different model and protected branch has its own set of secrets which allows for flexibility
+between different pipelines.
+
+For example, for the branch sap-dev, the prefix=SAP_DEV, and the following secrets are:
+
+- {prefix}_ECR_URI, which is the URI of the ECR repository to push to. For example, for the
+   sap change model this is the lambda-sap-prediction-dev repository.
+- {prefix}_DOMAIN_NAME, is the custom domain name. This is likely going to be the same across the different
+   models, but is still included in the secrets for flexibility.
+- {prefix}_DATA_BUCKET, is the name of the s3 data bucket where data to be scored by the model is stored
+- {prefix}_MODEL_BUCKET, is the name of the s3 bucket where the model is stored
+- {prefix}_PREDICTIONS_BUCKET, is the name of the s3 bucket where the predictions are stored
+
+
+# Building and Testing the Prediction Lambda Function Locally
+TODO: Generalise these instructions for the various different pipelines
+
+This guide outlines the steps to build and test the Lambda function locally using Docker. These instructions assume you're working with a machine that has Docker installed.
+
+### Prerequisites
+Docker: Make sure Docker is installed and running on your machine.
+AWS Credentials: Ensure you have AWS credentials set up on your local machine, typically stored
+in ~/.aws/credentials.
+Root Directory: All commands should be run from the root directory of the repository.
+Step-by-Step Guide
+1. Building the Docker Image
+First, navigate to the root directory of the repository. Open a terminal and execute the following
+2. command to build the Docker image:
+
+```bash
+docker build -t sap -f deployment/Dockerfile.prediction.lambda .
+```
+
+This will build a Docker image tagged as sap_change using the Dockerfile.prediction.lambda located
+in the deployment directory.
+
+2. Running the Docker Image
+Once the image is built, you can run it using the following command:
+
+```bash
+docker run -p 9000:8080 -v ~/.aws/credentials:/root/.aws/credentials:ro -e RUNTIME_ENVIRONMENT=dev -e PREDICTIONS_BUCKET=retrofit-sap-predictions-dev sap
+```
+This command does the following:
+
+Maps port 9000 on your local machine to port 8080 on the Docker container.
+Mounts your AWS credentials into the Docker container in read-only mode.
+Sets the RUNTIME_ENVIRONMENT variable to dev.
+3. Testing the Lambda Function
+To test the Lambda function, use the following curl command:
+
+```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\"}"}'
+```
+
+This will send a POST request to the running Lambda function and pass in the required data as JSON.

deployment/.dockerignore

@@ -0,0 +1,9 @@
+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/__pycache__
+modules/ml-pipeline/src/.dvc
+modules/ml-pipeline/src/analysis
+modules/ml-pipeline/src/metrics

deployment/Dockerfile.prediction.lambda

@@ -0,0 +1,25 @@
+FROM public.ecr.aws/lambda/python:3.10
+
+# Set the working directory
+WORKDIR ${LAMBDA_TASK_ROOT}
+ENV PYTHONPATH "${PYTHONPATH}:${LAMBDA_TASK_ROOT}"
+
+# Environment variables
+ARG RUNTIME_ENVIRONMENT
+ENV RUNTIME_ENVIRONMENT=${RUNTIME_ENVIRONMENT}
+
+# Install necessary build tools - required to test locally
+RUN 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 --no-cache-dir -r ./requirements.txt
+
+# Copy the project code
+COPY modules/ml-pipeline/src/pipeline ./pipeline
+# Copy the handler
+COPY deployment/handlers/prediction_app.py ./pipeline/prediction_app.py
+WORKDIR ${LAMBDA_TASK_ROOT}/pipeline
+
+
+CMD [ "prediction_app.handler" ]

deployment/handlers/prediction_app.py

@@ -0,0 +1,123 @@
+"""
+This script is the handler for the lambda prediction function, responsible
+for producting predictions for a model
+"""
+
+import boto3
+from botocore.exceptions import NoCredentialsError
+import json
+from io import StringIO
+import os
+import logging
+from generate_predictions import generate_predictions
+from core.MLModels import model_factory
+from config import settings
+from core.DataClient import dataclient_factory
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+
+PREDICTIONS_BUCKET = os.getenv("PREDICTIONS_BUCKET", None)
+
+
+def upload_dataframe_to_s3(df, bucket, s3_file_name):
+    """
+    Upload a pandas DataFrame to an S3 bucket as CSV
+
+    :param df: DataFrame to upload
+    :param bucket: Bucket to upload to
+    :param s3_file_name: S3 object name
+    :return: True if file was uploaded, else False
+    """
+
+    # Initialize the S3 client
+    s3 = boto3.client("s3")
+    csv_buffer = StringIO()
+
+    # Write the DataFrame to the buffer as CSV
+    df.to_csv(csv_buffer, index=False)
+
+    try:
+        # Upload the CSV from the buffer to S3
+        s3.put_object(Bucket=bucket, Key=s3_file_name, Body=csv_buffer.getvalue())
+        print(f"Successfully uploaded DataFrame to {bucket}/{s3_file_name}")
+        return True
+    except NoCredentialsError:
+        print("Credentials not available")
+        return False
+
+
+def handler(event, context):
+    """
+    Take in event and trigger the prediction pipeline
+    """
+
+    logger.info("received event: " + str(event))
+
+    try:
+        body = (
+            json.loads(event["body"])
+            if not isinstance(event["body"], dict)
+            else event["body"]
+        )
+
+        property_id = body["property_id"]
+        portfolio_id = body["portfolio_id"]
+        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
+        client_params = settings.client
+        feature_process_params = settings.feature_processor
+        generate_predictions_params = settings.generate_predictions
+
+        model = model_factory(build_model_params["model_type"])
+
+        logger.info(f"--- Initiate Input DataClient ---")
+        input_dataclient = dataclient_factory(
+            dataclient_type="aws-s3",
+            dataclient_config=client_params["aws-s3"],
+        )
+
+        logger.info(f"--- Initiate Output DataClient ---")
+        output_dataclient = dataclient_factory(
+            dataclient_type="aws-s3",
+            dataclient_config=client_params["aws-s3"],
+        )
+
+        generate_predictions(
+            input_dataclient=input_dataclient,
+            output_dataclient=output_dataclient,
+            model=model,
+            target=feature_process_params["feature_processor_config"]["target"],
+            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[
+                "predictions_column_name"
+            ],
+            identifier_column=generate_predictions_params["identifier_column"],
+        )
+
+        return {
+            "statusCode": 200,
+            "body": json.dumps(
+                {
+                    "message": "Successfully processed input",
+                    "storage_filepath": storage_filepath,
+                }
+            ),
+        }
+
+    except (Exception, KeyError, ValueError) as e:
+        logger.info("Prediction failed")
+        logger.info(e)
+        return {
+            "statusCode": 500,
+            "body": json.dumps({"message": "Prediction failed", "error": str(e)}),
+        }

deployment/serverless.yml

@@ -0,0 +1,53 @@
+service: ${env:STACK_NAME}
+
+provider:
+  name: aws
+  region: eu-west-2
+  architecture: x86_64
+  environment:
+    RUNTIME_ENVIRONMENT: ${env:RUNTIME_ENVIRONMENT}
+    PREDICTIONS_BUCKET: ${env:PREDICTIONS_BUCKET}
+    DATA_BUCKET: ${env:DATA_BUCKET}
+    DOMAIN_NAME: ${env:DOMAIN_NAME}
+    ECR_URI: ${env:ECR_URI}
+    GITHUB_SHA: ${env:GITHUB_SHA}
+  iam:
+    role:
+      name: ${env:STACK_NAME}_s3_access
+      statements:
+        # Allow reading from the DATA_BUCKET
+        - Effect: Allow
+          Action:
+            - s3:*
+          Resource:
+            - arn:aws:s3:::${env:DATA_BUCKET}
+            - arn:aws:s3:::${env:DATA_BUCKET}/*
+        # Allow reading and writing to PREDICTIONS_BUCKET
+        - Effect: Allow
+          Action:
+            - s3:*
+          Resource:
+            - arn:aws:s3:::${env:PREDICTIONS_BUCKET}
+            - arn:aws:s3:::${env:PREDICTIONS_BUCKET}/*
+
+
+
+plugins:
+  - serverless-domain-manager
+
+custom:
+  customDomain:
+    domainName: api.${self:provider.environment.DOMAIN_NAME}
+    basePath: ${env:STACK_NAME}
+    createRoute53Record: true
+    certificateArn: ${ssm:/ssl_certificate_arn}
+
+functions:
+  sap_prediction_lambda:
+    image:
+      uri: ${env:ECR_URI}:${env:GITHUB_SHA}
+    events:
+      - http:
+          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

modules/ml-pipeline/.dvc/config

@@ -1,2 +0,0 @@
-['remote "myremote"']
-    url = /tmp/dvcstore

modules/ml-pipeline/.gitignore

@@ -1,4 +1,6 @@
 .dev_env/
+.dev_env_pipeline/
 __pycache__/
 .DS_Store
 .vscode/
+data/

modules/ml-pipeline/.gto

@@ -1,2 +0,0 @@
-# .gto config file
-stages: [dev, stage, prod] # list of allowed Stages

modules/ml-pipeline/.pre-commit-config.yaml

@@ -20,6 +20,6 @@ repos:
     hooks:
     -   id: dvc-push-experiment
         name: DVC - Push to experiment to remote location (experiments)
-        entry: bash -c 'cd modules/ml-pipeline/src/pipeline/src && dvc push -r experiments || echo "Up to date!"'
+        entry: bash -c 'cd modules/ml-pipeline/src/pipeline && dvc push -r experiments || echo "Up to date!"'
         language: system
         verbose: true

modules/ml-pipeline/Makefile

@@ -1,9 +1,25 @@
 export PYENV_ROOT=$(HOME)/.pyenv
 export PATH := $(PYENV_ROOT)/bin:$(PATH)
 PYTHON_VERSION ?= 3.10.12
+CONDA_ENV=dev_env_pipeline
 
 .PHONY: init
-init: dev-pyenv
+init: dev-conda
+
+.PHONY: dev-conda
+dev-conda:
+	# conda deactivate || echo "Not in conda environment"
+	# 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
+	echo "TO ACTIVATE ENVIRONMENT, USE THE FOLLOWING COMMAND"
+	echo "conda activate ${CONDA_ENV}"
+
 
 .PHONY: dev-pyenv
 dev-pyenv:
@@ -11,7 +27,7 @@ dev-pyenv:
 	pyenv install ${PYTHON_VERSION} || echo "Python version already installed"
 	pyenv global ${PYTHON_VERSION}
 	python3 -m venv .dev_env_pipeline
-	. .dev_env_pipeline/bin/activate && pip install --upgrade pip && pip install -r src/pipeline/src/requirements/training/requirements-dev.txt && pip install -r src/pipeline/src/requirements/version_control/requirements.txt && pre-commit install
+	. .dev_env_pipeline/bin/activate && pip install --upgrade pip && pip install -r src/pipeline/requirements/training/requirements-dev.txt && pip install -r src/pipeline/requirements/version_control/requirements.txt && pre-commit install
 	echo "TO ACTIVATE ENVIRONMENT, USE THE FOLLOWING COMMAND"
 	echo "source .dev_env_pipeline/bin/activate"
 

modules/ml-pipeline/README.MD

@@ -1,16 +1,30 @@
 # ML-pipeline
 
-This is a dummy ML-pipeline, consisting of:
+This is a generic ML-pipeline, consisting of:
 - dvc tracking for version control (data and models)
 - gto for model registry
 - docs, created via sphinx (in pre-commit hooks)
 - tests for unit, integration and end to end testing
 
 Within `src` folder, the structure is as follows:
-- multiple pipelines can be defined
+- `pipeline` folder, which contains all the codebase for the generic pipeline
-    - i.e. for a product, we might require multuple pipelines do deliver a result
+    - The pipeline can track multiple models through dvc and gto model registry
-    - i.e. multiple models
+- Deployment files:
-- these models can be all tracked within the same gto model registry
+    - Prediction.Dockerfile - code to create the prediction deployment image
+    - Training.Dockerfil - code to create the training image (i.e. for remote training on EC2/ Fargate)
+- Docker development environment:
+    - If you wish to develop within a docker.
+
+# 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
+- 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 enable the virtual envrionemnt created in vscode:
 - Open settings

modules/ml-pipeline/src/.dockerignore

@@ -0,0 +1,8 @@
+pipeline/data/predictions
+pipeline/data/fit_predictions
+pipeline/data/prepared_data/train.parquet
+pipeline/data/fit_predictions
+pipeline/data/model/allmodels
+pipeline/metrics
+pipeline/.dvc
+pipeline/analysis

modules/ml-pipeline/src/Prediction.Dockerfile

@@ -1,14 +1,16 @@
 # 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
 
-COPY src/requirements/predictions/requirements.txt requirements.txt
+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
 
 # 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 src/ /home/src/
+COPY pipeline/ /home/pipeline/
 
-WORKDIR /home/src/
+WORKDIR /home/pipeline/
 
-CMD [ "python", "generate_predictions.py"]
+CMD [ "python", "3_generate_predictions.py"]

modules/ml-pipeline/src/README.md

@@ -0,0 +1,3 @@
+# The generic reproducible ML-pipeline
+
+Pipeline required to build a model to produce an output, that gets hashed via DVC

modules/ml-pipeline/src/pipeline/.gitignore

@@ -0,0 +1,3 @@
+
+# Ignore dynaconf secret files
+.secrets.*

modules/ml-pipeline/src/pipeline/0_startup_cleanup.py

@@ -6,9 +6,9 @@ import shutil
 import yaml
 from pathlib import Path
 from core.Logger import logger
+from config import settings
 
-startup_cleanup_path = Path(__file__).parent / "configs" / "startup_cleanup.yaml"
+startup_cleanup_params = settings.startup_cleanup
-startup_cleanup_params = yaml.safe_load(open(startup_cleanup_path))
 
 
 def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
@@ -16,13 +16,9 @@ def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
     Remove the directory where artefacts are stored
     """
 
-    logger.info("---------------------")
     logger.info(f"--- Run Clean up ---")
-    logger.info("---------------------")
 
-    logger.info("-------------------------")
     logger.info(f"--- Delete artefacts ---")
-    logger.info("-------------------------")
 
     artefact_directory_path = Path(artefacts_directory)
 
@@ -31,9 +27,7 @@ def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
         logger.info(f"Removing the directory: {artefacts_directory}")
         shutil.rmtree(artefact_directory_path)
 
-    logger.info("-----------------------")
     logger.info(f"--- Delete metrics ---")
-    logger.info("-----------------------")
 
     metrics_directory_path = Path(metrics_directory)
 
@@ -45,15 +39,11 @@ def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
     run_cleanup(
         artefacts_directory=startup_cleanup_params["artefacts"],
         metrics_directory=startup_cleanup_params["metrics"],
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

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

@@ -15,17 +15,41 @@ from configs.feature_processor_logic import business_logic, new_feature_funcs
 from core.Logger import logger
 from core.DataClient import dataclient_factory
 from core.FeatureProcessor import feature_processor_factory
+from config import settings
+
+logger.info(f"--- Initiate Parameters ---")
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
-client_path = Path(__file__).parent / "configs" / "client.yaml"
+client_params = settings.client
-client_params = yaml.safe_load(open(client_path))
+prepare_data_params = settings.prepare_data
+feature_process_params = settings.feature_processor
 
-prepare_data_path = Path(__file__).parent / "configs" / "prepare_data.yaml"
+data_filepath = prepare_data_params["data_filepath"]
-prepare_data_params = yaml.safe_load(open(prepare_data_path))
+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"]
+feature_processor_config = feature_process_params["feature_processor_config"]
 
-feature_process_path = Path(__file__).parent / "configs" / "feature_processor.yaml"
+logger.info(f"--- Initiate DataClient ---")
-feature_process_params = yaml.safe_load(open(feature_process_path))
+
+input_dataclient_type = prepare_data_params["input_dataclient_type"]
+output_dataclient_type = prepare_data_params["output_dataclient_type"]
+
+input_dataclient = dataclient_factory(
+    dataclient_type=input_dataclient_type,
+    dataclient_config=client_params[input_dataclient_type],
+)
+output_dataclient = dataclient_factory(
+    dataclient_type=output_dataclient_type,
+    dataclient_config=client_params[output_dataclient_type],
+)
+
+logger.info(f"--- Initiate FeatureProcessor ---")
+
+feature_processor = feature_processor_factory(
+    feature_process_params["feature_processor_type"]
+)
 
 
 def prepare_data(
@@ -46,15 +70,11 @@ def prepare_data(
     :param pipeline_mode: bool, Default False, this caches out the file for experimentation, objects returned in pipeline mode
     """
 
-    logger.info("--------------------")
     logger.info("--- Loading data ---")
-    logger.info("--------------------")
 
     data = input_dataclient.load_data(location=data_filepath, load_config={})
 
-    logger.info("--------------------------")
     logger.info("--- Feature Processing ---")
-    logger.info("--------------------------")
 
     data = feature_processor.feature_process(
         data,
@@ -63,77 +83,51 @@ def prepare_data(
         new_feature_funcs=new_feature_funcs,
     )
 
-    logger.info("----------------------")
     logger.info("--- Splitting data ---")
-    logger.info("----------------------")
 
     if train_proportion == 1:
         train = data
-        test = None
+        # Sample 10% of the data for testing
+        test = data.sample(round(len(data) * 0.1))
     else:
         train, test = train_test_split(
             data, train_size=train_proportion, test_size=(1 - train_proportion)
         )
+        test = test.reset_index(drop=True)
+
+    train = train.reset_index(drop=True)
 
-    logger.info("-----------------------")
     logger.info("--- Outputting data ---")
-    logger.info("-----------------------")
 
-    output_dataclient.save_data(obj=train, location=output_train_filepath)
+    output_dataclient.save_data(
+        obj=train, location=output_train_filepath, save_config=None
+    )
 
     if test is not None:
-        output_dataclient.save_data(obj=test, location=output_test_filepath)
+        output_dataclient.save_data(
+            obj=test, location=output_test_filepath, save_config=None
+        )
 
     return train, test
 
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
-    logger.info("----------------------------")
-    logger.info(f"--- Initiate DataClient ---")
-    logger.info("----------------------------")
-
-    input_dataclient_type = prepare_data_params["input_dataclient_type"]
-    output_dataclient_type = prepare_data_params["output_dataclient_type"]
-
-    input_dataclient = dataclient_factory(
-        dataclient_type=input_dataclient_type,
-        dataclient_config=client_params[input_dataclient_type],
-    )
-    output_dataclient = dataclient_factory(
-        dataclient_type=output_dataclient_type,
-        dataclient_config=client_params[output_dataclient_type],
-    )
-
-    logger.info("----------------------------------")
-    logger.info(f"--- Initiate FeatureProcessor ---")
-    logger.info("----------------------------------")
-
-    feature_processor = feature_processor_factory(
-        feature_process_params["feature_processor_type"]
-    )
-
-    logger.info("---------------------------")
     logger.info(f"--- Prepare Data Stage ---")
-    logger.info("---------------------------")
 
     prepare_data(
         input_dataclient=input_dataclient,
         output_dataclient=output_dataclient,
         feature_processor=feature_processor,
-        data_filepath=prepare_data_params["data_filepath"],
+        data_filepath=data_filepath,
-        train_proportion=prepare_data_params["train_proportion"],
+        train_proportion=train_proportion,
-        output_train_filepath=prepare_data_params["output_train_filepath"],
+        output_train_filepath=output_train_filepath,
-        output_test_filepath=prepare_data_params["output_test_filepath"],
+        output_test_filepath=output_test_filepath,
-        feature_processor_config=feature_process_params["feature_processor_config"],
+        feature_processor_config=feature_processor_config,
         business_logic=business_logic,
         new_feature_funcs=new_feature_funcs,
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

modules/ml-pipeline/src/pipeline/2_build_model.py

@@ -0,0 +1,149 @@
+"""
+Second Pipieline step:
+Once we have the features, we build a model
+"""
+
+import os
+import yaml
+import pandas as pd
+from typing import Union, List
+from pathlib import Path
+from core.Logger import logger
+from core.interface.InterfaceMetrics import MLMetrics
+from core.interface.InterfaceModels import MLModel
+from core.interface.InterfaceDataClient import DataClient
+from core.DataClient import dataclient_factory
+from core.MLModels import model_factory
+from core.MLMetrics import metrics_factory
+from configs.post_prediction_logic import post_prediction_logic
+from config import settings
+
+logger.info(f"--- Initiate Parameters ---")
+
+RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
+
+prepare_data_params = settings.prepare_data
+build_model_params = settings.build_model
+feature_process_params = settings.feature_processor
+generate_metrics_params = settings.generate_metrics
+generate_predictions_params = settings.generate_predictions
+
+model_type = build_model_params["model_type"]
+target = feature_process_params["feature_processor_config"]["target"]
+fit_predictions_filepath = build_model_params["fit_predictions_filepath"]
+predictions_column_name = generate_predictions_params["predictions_column_name"]
+identifier_columns = feature_process_params["feature_processor_config"][
+    "identifier_columns"
+]
+model_save_location = build_model_params["model_save_filepath"]
+model_hyperparameters = build_model_params[model_type]
+train_filepath = prepare_data_params["output_train_filepath"]
+test_filepath = prepare_data_params["output_test_filepath"]
+fit_metrics_filepath = build_model_params["fit_metrics_filepath"]
+
+logger.info(f"--- Initiate DataClient ---")
+
+# Output of previous prepare data step, will be where the data is
+dataclient = dataclient_factory(prepare_data_params["output_dataclient_type"])
+
+logger.info(f"--- Initiate MLModel ---")
+
+model = model_factory(model_type)
+
+logger.info(f"--- Initiate Metrics ---")
+
+metrics = metrics_factory(generate_metrics_params["metrics_type"])
+
+
+def build_model(
+    dataclient: DataClient,
+    model: MLModel,
+    metrics: MLMetrics,
+    target: str,
+    identifier_columns: List[str],
+    model_save_location: str,
+    model_hyperparameters: dict,
+    fit_predictions_filepath: str,
+    predictions_column_name: str,
+    fit_metrics_filepath: str,
+    train_filepath: Union[str, None] = None,
+    test_filepath: Union[str, None] = None,
+    train_data: Union[pd.DataFrame, None] = None,
+    test_data: Union[pd.DataFrame, None] = None,
+    pipeline_mode: bool = False,
+):
+    logger.info("--- Loading Data for build process ---")
+
+    if train_data is None:
+        if train_filepath is None:
+            raise ValueError(f"Need {train_filepath} if no data supplied")
+        train_data = dataclient.load_data(location=train_filepath, load_config=None)
+
+    if test_data is None:
+        if test_filepath is None:
+            raise ValueError(f"Need {test_filepath} if no data supplied")
+        test_data = dataclient.load_data(location=test_filepath, load_config=None)
+
+    logger.info("--- Training model ---")
+
+    model.train_model(
+        data=train_data.drop(columns=identifier_columns),
+        target=target,
+        model_hyperparameters=model_hyperparameters,
+    )
+
+    logger.info("--- Generating fit predictions ---")
+
+    fit_predictions = model.predict(
+        data=train_data, post_prediction_logic=post_prediction_logic
+    )
+
+    logger.info("--- Saving fit predictions ---")
+
+    predictions_df = pd.DataFrame(fit_predictions)
+    predictions_df.columns = [predictions_column_name]
+
+    dataclient.save_data(
+        obj=predictions_df, location=fit_predictions_filepath, save_config=None
+    )
+
+    logger.info("--- Generating fit metrics ---")
+
+    metrics_output = metrics.generate_metrics(
+        target=train_data[target],
+        predictions=pd.Series(fit_predictions),
+    )
+
+    logger.info("--- Saving model ---")
+
+    model.save_model(path=Path(model_save_location))
+
+    logger.info("--- Saving fit metrics ---")
+
+    dataclient.save_data(
+        obj=metrics_output, location=fit_metrics_filepath, save_config=None
+    )
+
+
+if __name__ == "__main__":
+
+    logger.info(f"--- {__file__} - Start! ---")
+
+    logger.info(f"--- Build Model Stage ---")
+
+    build_model(
+        dataclient=dataclient,
+        model=model,
+        metrics=metrics,
+        target=target,
+        identifier_columns=identifier_columns,
+        model_save_location=model_save_location,
+        model_hyperparameters=model_hyperparameters,
+        train_filepath=train_filepath,
+        test_filepath=test_filepath,
+        fit_metrics_filepath=fit_metrics_filepath,
+        fit_predictions_filepath=fit_predictions_filepath,
+        predictions_column_name=predictions_column_name,
+    )
+
+    logger.info(f"--- {__file__} - Complete! ---")

modules/ml-pipeline/src/pipeline/3_generate_predictions.py

@@ -0,0 +1,71 @@
+"""
+Third part of the pipeline:
+After the model is built, we can evaluate its performance
+"""
+
+import os
+from core.DataClient import dataclient_factory
+from core.MLModels import model_factory
+from core.Logger import logger
+from config import settings
+from generate_predictions import generate_predictions
+
+logger.info(f"--- Initiate Parameters ---")
+
+RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
+
+client_params = settings.client
+prepare_data_params = settings.prepare_data
+build_model_params = settings.build_model
+generate_predictions_params = settings.generate_predictions
+feature_process_params = settings.feature_processor
+
+input_dataclient_type = generate_predictions_params["input_dataclient_type"]
+output_dataclient_type = generate_predictions_params["output_dataclient_type"]
+
+test_data_filepath = generate_predictions_params["test_data_filepath"]
+test_data_filepath = os.environ.get("PREDICTION_FILE", test_data_filepath)
+
+target = feature_process_params["feature_processor_config"]["target"]
+model_filepath = build_model_params["model_save_filepath"]
+predictions_output_filepath = generate_predictions_params["predictions_output_filepath"]
+predictions_column_name = generate_predictions_params["predictions_column_name"]
+
+logger.info(f"--- Initiate MLModel ---")
+
+model = model_factory(build_model_params["model_type"])
+
+logger.info(f"--- Initiate DataClient ---")
+
+# We may have different locations of loading hence why we use one specified in generate_predictions.yaml
+# I.e. for metric runs, this will be a local data client
+# For predictions, we will want a cloud data client
+input_dataclient = dataclient_factory(
+    dataclient_type=input_dataclient_type,
+    dataclient_config=client_params[input_dataclient_type],
+)
+
+output_dataclient = dataclient_factory(
+    dataclient_type=output_dataclient_type,
+    dataclient_config=client_params[output_dataclient_type],
+)
+
+
+if __name__ == "__main__":
+
+    logger.info(f"--- {__file__} - Start! ---")
+
+    logger.info(f"--- Generate Predictions Stage---")
+
+    generate_predictions(
+        input_dataclient=input_dataclient,
+        output_dataclient=output_dataclient,
+        model=model,
+        target=target,
+        model_filepath=model_filepath,
+        test_data_filepath=test_data_filepath,
+        predictions_output_filepath=predictions_output_filepath,
+        predictions_column_name=predictions_column_name,
+    )
+
+    logger.info(f"--- {__file__} - Complete! ---")

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

@@ -0,0 +1,113 @@
+"""
+Third part of the pipeline:
+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
+from core.DataClient import dataclient_factory
+from core.MLModels import model_factory
+from core.MLMetrics import metrics_factory
+from core.Logger import logger
+from config import settings
+
+logger.info(f"--- Initiate Parameters ---")
+
+RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
+
+client_params = settings.client
+prepare_data_params = settings.prepare_data
+build_model_params = settings.build_model
+generate_predictions_params = settings.generate_predictions
+generate_metrics_params = settings.generate_metrics
+feature_process_params = settings.feature_processor
+
+target = feature_process_params["feature_processor_config"]["target"]
+test_data_filepath = generate_predictions_params["test_data_filepath"]
+predictions_output_filepath = generate_predictions_params["predictions_output_filepath"]
+predictions_column_name = generate_predictions_params["predictions_column_name"]
+metrics_output_filepath = generate_metrics_params["metrics_output_filepath"]
+
+logger.info(f"--- Initiate MLModel ---")
+
+model = model_factory(build_model_params["model_type"])
+
+logger.info(f"--- Initiate DataClient ---")
+
+# Use data client for input and output, as we use dvc to cache later to the cloud
+dataclient_type = generate_metrics_params["dataclient_type"]
+dataclient = dataclient_factory(
+    dataclient_type=dataclient_type,
+    dataclient_config=client_params[dataclient_type],
+)
+
+logger.info(f"--- Initiate MLMetrics ---")
+
+metrics = metrics_factory(generate_metrics_params["metrics_type"])
+
+
+def generate_metrics(
+    input_dataclient: DataClient,
+    output_dataclient: DataClient,
+    model: MLModel,
+    metrics: MLMetrics,
+    target: str,
+    test_data_filepath: str,
+    predictions_output_filepath: str,
+    predictions_column_name: str,
+    metrics_output_filepath: str,
+):
+    """
+    For a given model, we generate prediction and evaluate this against the true target
+    """
+
+    logger.info("--- Loading test data ---")
+
+    test_data = input_dataclient.load_data(
+        location=test_data_filepath, load_config=None
+    )
+
+    logger.info("--- Loading predictions ---")
+
+    predictions = input_dataclient.load_data(
+        location=predictions_output_filepath, load_config=None
+    )
+
+    logger.info("--- Generating metrics ---")
+
+    metrics_output = metrics.generate_metrics(
+        target=test_data[target],
+        predictions=pd.Series(predictions[predictions_column_name]),
+    )
+
+    logger.info("--- Saving metrics ---")
+
+    output_dataclient.save_data(
+        obj=metrics_output, location=metrics_output_filepath, save_config=None
+    )
+
+
+if __name__ == "__main__":
+
+    logger.info(f"--- {__file__} - Start! ---")
+
+    logger.info(f"--- Generate Metrics Stage---")
+
+    generate_metrics(
+        input_dataclient=dataclient,
+        output_dataclient=dataclient,
+        model=model,
+        metrics=metrics,
+        target=target,
+        test_data_filepath=test_data_filepath,
+        predictions_output_filepath=predictions_output_filepath,
+        predictions_column_name=predictions_column_name,
+        metrics_output_filepath=metrics_output_filepath,
+    )
+
+    logger.info(f"--- {__file__} - Complete! ---")

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

@@ -0,0 +1,162 @@
+"""
+Fourth part of the pipeline:
+After the model is built and metrics are generated,
+we want to test this model against known scenarios
+"""
+
+import os
+import pandas as pd
+from core.interface.InterfaceModels import MLModel
+from core.interface.InterfaceDataClient import DataClient
+from core.interface.InterfaceMetrics import MLMetrics
+from configs.post_prediction_logic import post_prediction_logic
+from core.DataClient import dataclient_factory
+from core.MLModels import model_factory
+from core.MLMetrics import metrics_factory
+from core.Logger import logger
+from config import settings
+
+logger.info(f"--- Initiate Parameters ---")
+
+RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
+
+client_params = settings.client
+prepare_data_params = settings.prepare_data
+build_model_params = settings.build_model
+generate_predictions_params = settings.generate_predictions
+generate_metrics_params = settings.generate_metrics
+feature_process_params = settings.feature_processor
+scenarios_params = settings.scenarios
+
+model_filepath = build_model_params["model_save_filepath"]
+target = feature_process_params["feature_processor_config"]["target"]
+scenario_data_filepaths = scenarios_params["scenario_data_filepaths"]
+predictions_column_name = generate_predictions_params["predictions_column_name"]
+comparison_output_filepath = scenarios_params["comparison_output_filepath"]
+metrics_output_filepath = scenarios_params["metrics_output_filepath"]
+
+logger.info(f"--- Initiate MLModel ---")
+
+model = model_factory(build_model_params["model_type"])
+
+logger.info(f"--- Initiate DataClient ---")
+
+# Use data client for input and output, as we use dvc to cache later to the cloud
+input_dataclient_type = scenarios_params["input_dataclient_type"]
+input_dataclient = dataclient_factory(
+    dataclient_type=input_dataclient_type,
+    dataclient_config=client_params[input_dataclient_type],
+)
+
+output_dataclient_type = scenarios_params["output_dataclient_type"]
+output_dataclient = dataclient_factory(
+    dataclient_type=output_dataclient_type,
+    dataclient_config=client_params[output_dataclient_type],
+)
+
+logger.info(f"--- Initiate MLMetrics ---")
+
+metrics = metrics_factory(generate_metrics_params["metrics_type"])
+
+
+def generate_scenario_predictions(
+    input_dataclient: DataClient,
+    output_dataclient: DataClient,
+    model: MLModel,
+    metrics: MLMetrics,
+    model_filepath: str,
+    scenario_data_filepaths: list,
+    predictions_column_name: str,
+    comparison_output_filepath: str,
+    metrics_output_filepath: str,
+):
+    """
+    Given the new model, we generate prediction for expected scenarios
+    """
+
+    logger.info("--- Loading Scenario Data ---")
+
+    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(
+            [
+                scenario_data,
+                input_dataclient.load_data(scenario_data_filepath, load_config=None),
+            ]
+        )
+
+    logger.info("--- Loading Model ---")
+
+    model.load_model(model_filepath)
+
+    logger.info("--- Generating Predictions ---")
+
+    predictions = model.predict(
+        data=scenario_data, post_prediction_logic=post_prediction_logic
+    )
+
+    logger.info("--- Generate Scenario Predicted Impact ---")
+
+    predictions_df = pd.DataFrame(predictions)
+    predictions_df.columns = [predictions_column_name]
+
+    scenario_data = pd.concat([scenario_data, predictions_df], axis=1)
+    scenario_data["predicted_impact"] = abs(
+        scenario_data[predictions_column_name] - scenario_data["sap_starting"]
+    )
+
+    logger.info("--- Generate Metrics ---")
+
+    metrics_dict = metrics.generate_metrics(
+        scenario_data["impact"], scenario_data["predicted_impact"]
+    )
+
+    metrics_df = pd.DataFrame(metrics_dict, index=[0]).T.reset_index()
+    metrics_df.columns = ["metric", "value"]
+
+    logger.info("--- Save prediction into metrics ---")
+
+    output_df = scenario_data[["uprn", "id", "impact", "predicted_impact"]]
+
+    output_dataclient.save_data(
+        obj=output_df, location=comparison_output_filepath, save_config=None
+    )
+
+    output_dataclient.save_data(
+        obj=metrics_df, location=metrics_output_filepath, save_config=None
+    )
+
+
+if __name__ == "__main__":
+
+    logger.info(f"--- {__file__} - Start! ---")
+
+    logger.info(f"--- Generate Scenario Predictions ---")
+
+    generate_scenario_predictions(
+        input_dataclient=input_dataclient,
+        output_dataclient=output_dataclient,
+        model=model,
+        metrics=metrics,
+        model_filepath=model_filepath,
+        scenario_data_filepaths=scenario_data_filepaths,
+        predictions_column_name=predictions_column_name,
+        comparison_output_filepath=comparison_output_filepath,
+        metrics_output_filepath=metrics_output_filepath,
+    )
+
+    logger.info(f"--- {__file__} - Complete! ---")

modules/ml-pipeline/src/pipeline/README.md

@@ -1,3 +1,40 @@
-# Pipeline 1
+# Training
 
-Pipeline required to build a model to produce an output
+This folder contains the code base for training experimentation.
+
+To understand the pipeline, run `dvc dag`
+
+There are 4 main steps:
+- Preparing data
+    - This is loading data (locally or from s3)
+    - Splitting the data into train and validation
+    - Creating additional features (if needed)
+    - **Data is cached**
+        - This will be down to the dvc remote location
+- Build model
+    - For the prepared data, we build a model using our configurations
+    - Model is saved (locally or s3)
+    - **Model and fit metrics are cached**
+        - This will be down to the dvc remote location
+- Generate Predictions
+    - For the given model, we generate predictions on validation test data
+    - **Predictions are cached**
+        - This will be down to the dvc remote location
+- Generate Metrics
+    - For the given model, we generate metrics on validation data/test data
+    - **Metrics are cached**
+        - This will be down to the dvc remote location
+
+Workflow:
+- Use `dvc metrics show` to view current metrics score
+- Adjust parameters/ codebase
+- When happy with changes, use `dvc exp run` to trigger an experiment
+- Due to cache, only need stages are re-run
+- Use `dvc metrics diff` to check the change in metrics
+- Use `dvc exp show` to view all experiments
+    - NOTE: the last experiment will always be applied to the workspace!
+- After running experiments, you can apply the the best model to workspace using `dvc exp apply [EXPERIMENT_NAME]`
+    - This experiment will have the corresponding .dvc files for the hashed model and data
+- Use version control as normal
+    - git add, git commit etc
+- To revert change, use `git checkout {COMMIT_HASH}`, followed by `git switch -c {NEW_BRANCH_NAME}`

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

@@ -0,0 +1,15 @@
+from dynaconf import Dynaconf
+
+settings = Dynaconf(
+    environments=True,
+    envvar_prefix="DYNACONF",
+    settings_files=[
+        "./configs/settings.yaml",
+        "./configs/build_model.yaml",
+        "./configs/analysis.yaml",
+        "./configs/scenarios.yaml",
+    ],
+)
+
+# `envvar_prefix` = export envvars with `export DYNACONF_FOO=bar`.
+# `settings_files` = Load these files in the order.

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

@@ -0,0 +1,16 @@
+default:
+  model_analysis:
+    dataclient_type: local
+    feature_importance_filepath: ./analysis/feature_importance.parquet
+    permutation_subsample_amount: 1000
+    loss_fns: "mean_absolute_percentage_error"
+    feature_importance_column: importance
+    n_repeats: 5
+    figwidth: 7
+    figheight: 6
+
+  prediction_analysis:
+    dataclient_type: local
+    nshap_samples: 100 # how many samples to use to approximate each Shapely value, larger values will be slower
+    n_val: 30  # how many datapoints from validation data should we interpret predictions for, larger values will be slower
+    row_index: [20695, 50243, 7653] # index of an example datapoint

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

@@ -0,0 +1,22 @@
+default:
+  build_model:
+    model_type: AutogluonAutoML
+    model_save_filepath: ./data/model/optimised/
+    fit_metrics_filepath: ./metrics/fit_metrics.json
+    fit_predictions_filepath: ./data/fit_predictions/predictions.parquet
+
+    SKLearnLinearRegression: null
+
+    SKLearnSVMRegression:
+      kernel: "linear"
+
+    AutogluonAutoML:
+      output_filepath: ./data/model/allmodels/
+      problem_type: regression
+      eval_metric: mean_squared_error #mean_absolute_error
+      time_limit: 1800
+      presets: medium_quality
+      excluded_model_types: ['RF', 'CAT', 'NN_TORCH', 'KNN', 'XT']
+      infer_limit: 0.05
+      infer_limit_batch_size: 10000
+      ag_args_ensemble: {'num_folds_parallel': 2}

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

@@ -0,0 +1,75 @@
+"""
+During the feature processor step, we can apply additional business logic and feature generation by defining them here
+"""
+
+"""
+Business Logic dict + functions
+"""
+
+
+def remove_starting_columns(df):
+    keep_column_index = [
+        False if col_name.endswith("_starting") else True
+        for col_name in list(df.columns)
+    ]
+    keep_columns = df.columns[keep_column_index].to_list()
+    keep_columns.append("sap_starting")
+    df = df[keep_columns]
+    return df
+
+
+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 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
+
+
+def keep_flats(df):
+    df = df[df["property_type"] == "Flat"]
+    return df
+
+
+def keep_non_zero_rdsap(df):
+    df = df[df["rdsap_change"] != 0]
+    return df
+
+
+# def keep_ending_columns(df):
+#     ending_column_index = [ col_name.endswith("_ENDING") for col_name in list(df.columns)]
+#     keep_columns = df.columns[ending_column_index].to_list()
+#     keep_columns.append("SAP_STARTING")
+#     print(keep_columns)
+#     df = df[keep_columns]
+#     return df
+
+business_logic = {
+    # "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
+}
+
+"""
+New features dict + function
+"""
+
+
+# def SAP_ENDING(df):
+#     return df["SAP_STARTING"] + df["RDSAP_CHANGE"]
+
+
+# new_feature_funcs = {"SAP_ENDING": SAP_ENDING}
+new_feature_funcs = {}

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

@@ -0,0 +1,35 @@
+"""
+After predictions, we may want to apply some post processing to the predictions
+"""
+import pandas as pd
+
+
+def clip_predictions_to_minimum_value(
+    data: pd.DataFrame, predictions: pd.Series, minimum_value: int = 0
+) -> pd.Series:
+
+    series_name = predictions.name
+    predictions.name = "predictions"
+    predictions_df = pd.concat([data, predictions], axis=1)
+    # We expect all prediction to be atleast one point improvement
+    replace_index = (
+        predictions_df["sap_starting"] + minimum_value > predictions_df["predictions"]
+    )
+    predictions_df.loc[replace_index, "predictions"] = (
+        predictions_df.loc[replace_index, "sap_starting"] + minimum_value
+    )
+
+    predictions_new = predictions_df["predictions"]
+    predictions_new.name = series_name
+    return predictions_new
+
+
+# def round_predictions(data: pd.DataFrame, predictions: pd.Series) -> pd.Series:
+
+#     return predictions.round()
+
+
+post_prediction_logic = {
+    "clip_predictions_to_minimum_value": clip_predictions_to_minimum_value,
+    # "round_predictions": round_predictions
+}

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

@@ -0,0 +1,13 @@
+default:
+  scenarios:
+    input_dataclient_type: aws-s3
+    output_dataclient_type: local
+    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/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

@@ -0,0 +1,81 @@
+default:
+  startup_cleanup:
+    artefacts: ./data
+    metrics: ./metrics
+
+  client:
+    aws-s3:
+      AWS_ACCESS_KEY_ID: null # Use local credentials
+      AWS_SECRET_ACCESS_KEY: null # Use local credentials
+      ENDPOINT_URL: null # Use local credentials
+    aws-s3-mock:
+      AWS_ACCESS_KEY_ID: minio
+      AWS_SECRET_ACCESS_KEY: minio123
+      ENDPOINT_URL: http://localhost:9000
+    local:
+      null
+
+  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/2024-05-25-08-36-36/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
+
+  feature_processor:
+    feature_processor_type: dataframe
+    feature_processor_config:
+      subsample_amount: null
+      subsample_seed: 0
+      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", "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']
+
+  generate_predictions:
+    input_dataclient_type: local
+    output_dataclient_type: local
+    test_data_filepath: ./data/prepared_data/test.parquet
+    predictions_output_filepath: ./data/predictions/predictions.parquet
+    predictions_column_name: predictions
+    identifier_column: id
+
+  generate_metrics:
+    dataclient_type: local
+    metrics_type: Regression
+    metrics_output_filepath: ./metrics/metrics.json
+
+dev:
+  generate_predictions:
+      input_dataclient_type: aws-s3

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

@@ -8,7 +8,7 @@ import boto3
 import pandas as pd
 from pathlib import Path
 from io import BytesIO
-from typing import List, Union
+from typing import List, Union, Any
 from core.interface.InterfaceDataClient import DataClient
 from core.Logger import logger
 
@@ -105,7 +105,7 @@ class AWSS3Client:
 
     def save_data(
         self,
-        obj: object,
+        obj: Any,
         location: str,
         save_config: Union[dict, None] = None,
     ) -> None:
@@ -134,7 +134,7 @@ class AWSS3Client:
             obj=obj, location=location, save_config=save_config
         )
 
-    def _save_parquet(self, obj: object, location: str, save_config: dict):
+    def _save_parquet(self, obj: pd.DataFrame, location: str, save_config: dict):
         """
         Save object as parquet
         """
@@ -142,9 +142,15 @@ class AWSS3Client:
         buffer = BytesIO()
         obj.to_parquet(buffer, index=False)
 
+        # Reset the buffer position to the beginning
+        buffer.seek(0)
+
         bucket, key = location.strip("s3://").split("/", 1)
         self.client.upload_fileobj(buffer, bucket, key)
 
+        # Close the buffer
+        buffer.close()
+
     def _load_parquet(self, location: str, load_config: dict) -> pd.DataFrame:
         """
         Load a parquet file
@@ -239,7 +245,8 @@ class LocalClient:
 
         save_methods = {
             ".parquet": self._save_parquet,
-            ".json": self._save_json
+            ".json": self._save_json,
+            ".md": self._save_md,
             # "": _save_directory(**save_config),
             # ADD MORE save_methods HERE
         }
@@ -288,3 +295,10 @@ class LocalClient:
         # Write the contents of the buffer to the local file
         with open(location, "wb") as f:
             f.write(buffer.getvalue())
+
+    def _save_md(self, obj: pd.DataFrame, location: str, save_config: dict):
+        """
+        Save object as markdown
+        """
+
+        obj.to_markdown(location, **save_config)

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

@@ -9,7 +9,6 @@ Create additional features from the dataset
 import pandas as pd
 from typing import List, Callable, Union
 from core.interface.InterfaceFeatureProcessor import FeatureProcessor
-from core.Logger import logger
 
 
 def feature_processor_factory(feature_processor_type: str) -> FeatureProcessor:
@@ -110,7 +109,9 @@ class DataFrameFeatureProcessor:
 
         # TODO: to test
         for key, value in new_feature_funcs.items():
-            df[key] = value(df)
+            key_column = value(df)
+            key_column.name = key
+            df = pd.concat([df, key_column], axis=1)
 
         return df
 
@@ -135,6 +136,8 @@ class DataFrameFeatureProcessor:
             subsample_amount=feature_processor_config["subsample_amount"],
             subsample_seed=feature_processor_config["subsample_seed"],
         )
+        df = self.apply_business_logic(df, business_logic=business_logic)
+        df = self.generate_new_features(df, new_feature_funcs=new_feature_funcs)
         df = self.drop_unused_columns(
             df, drop_columns=feature_processor_config["drop_columns"]
         )
@@ -143,6 +146,4 @@ class DataFrameFeatureProcessor:
             retain_features=feature_processor_config["retain_features"],
             target=feature_processor_config["target"],
         )
-        df = self.apply_business_logic(df, business_logic=business_logic)
-        df = self.generate_new_features(df, new_feature_funcs=new_feature_funcs)
         return df

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

@@ -21,6 +21,7 @@ def setup_logger():
 
     # Add the stream handler to the logger
     logger.addHandler(stream_handler)
+    logger.propagate = False
 
     return logger
 

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

@@ -4,6 +4,7 @@ Implementation of MLMetrics, all of which will have two methods:
 - Generate Plot Suite
 """
 
+import numpy as np
 import pandas as pd
 from typing import Union
 from sklearn.metrics import (
@@ -14,6 +15,18 @@ from sklearn.metrics import (
 )
 from core.interface.InterfaceMetrics import MLMetrics
 
+# Define the function to return the SMAPE value
+def symmetric_mape(actual, predicted) -> float:
+
+    # Convert actual and predicted to numpy
+    # array data type if not already
+    if not all([isinstance(actual, np.ndarray), isinstance(predicted, np.ndarray)]):
+        actual, predicted = np.array(actual), np.array(predicted)
+
+    return np.mean(
+        np.abs(predicted - actual) / ((np.abs(predicted) + np.abs(actual)) / 2)
+    )
+
 
 def metrics_factory(metrics_type: str) -> MLMetrics:
     metrics = {
@@ -34,7 +47,7 @@ class RegressionMetrics:
         median_absolute_error,
         mean_squared_error,
         mean_absolute_percentage_error,
-        # max_error
+        symmetric_mape,
     ]
 
     def generate_metrics(

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

@@ -25,7 +25,7 @@ def model_factory(model_type: str) -> MLModel:
     models = {
         "SKLearnLinearRegression": SKLearnLinearRegression(),
         "SKLearnSVMRegression": SKLearnSVMRegression(),
-        "AutogluonAutoML": AutogluonAutoML()
+        "AutogluonAutoML": AutogluonAutoML(),
         # ADD OTHER MODELS HERE
     }
 
@@ -75,7 +75,9 @@ class SKLearnLinearRegression:
         y_train = data[target]
         self.model.fit(x_train, y_train)
 
-    def predict(self, data: pd.DataFrame) -> pd.Series:
+    def predict(
+        self, data: pd.DataFrame, post_prediction_logic: dict | None = None
+    ) -> pd.Series:
         """
         Method to predict
         """
@@ -128,7 +130,9 @@ class SKLearnSVMRegression:
         y_train = data[target]
         self.model.fit(x_train, y_train)
 
-    def predict(self, data: pd.DataFrame) -> pd.Series:
+    def predict(
+        self, data: pd.DataFrame, post_prediction_logic: dict | None = None
+    ) -> pd.Series:
         """
         Method to predict
         """
@@ -145,6 +149,9 @@ class AutogluonAutoML:
         "time_limit",
         "presets",
         "excluded_model_types",
+        "infer_limit",
+        "infer_limit_batch_size",
+        "ag_args_ensemble",
     ]
 
     def load_model(self, path: Union[Path, str]) -> None:
@@ -161,8 +168,12 @@ class AutogluonAutoML:
         if self.model is None:
             raise KeyError("No model trained/ loaded - unable to save")
 
-        logger.info("In local development mode - no need for s3 client")
+        logger.info(
-        logger.info("Using AutoGluon Model - Model saving already occured")
+            "Using AutoGluon Model - Model saving is using optimised deployment mode"
+        )
+
+        logger.info("Saving optimised model")
+        self.model.clone_for_deployment(str(path))
 
         return str(path)
 
@@ -195,17 +206,44 @@ class AutogluonAutoML:
             time_limit=model_hyperparameters["time_limit"],
             presets=model_hyperparameters["presets"],
             excluded_model_types=model_hyperparameters["excluded_model_types"],
+            infer_limit=model_hyperparameters["infer_limit"],
+            infer_limit_batch_size=model_hyperparameters["infer_limit_batch_size"],
+            ag_args_ensemble=model_hyperparameters["ag_args_ensemble"],
         )
 
-    def predict(self, data: pd.DataFrame) -> pd.Series:
+    def predict(
+        self, data: pd.DataFrame, post_prediction_logic: dict | None = None
+    ) -> pd.Series:
         """
         Method to predict
         """
 
+        if post_prediction_logic is None:
+            post_prediction_logic = {}
+
         if self.model is None:
             print("No model loaded/ trained")
             exit(1)
 
         predictions = pd.Series(self.model.predict(data))
 
+        if len(post_prediction_logic) != 0:
+            predictions = self._apply_post_prediction_logic(
+                data=data,
+                predictions=predictions,
+                post_prediction_logic=post_prediction_logic,
+            )
+
+        return predictions
+
+    def _apply_post_prediction_logic(
+        self, data: pd.DataFrame, predictions: pd.Series, post_prediction_logic: dict
+    ):
+        """
+        For predictions, we can apply post processing logic to clean up predictions
+        """
+
+        for _, value in post_prediction_logic.items():
+            predictions = value(data, predictions)
+
         return predictions

modules/ml-pipeline/src/pipeline/core/interface/InterfaceDataClient.py

@@ -3,8 +3,7 @@ Interface for all DataClient i.e. s3, database, local etc
 """
 
 import pandas as pd
-from io import BytesIO
+from typing import Protocol, Union, Any
-from typing import Protocol, Union
 
 
 class DataClient(Protocol):
@@ -22,9 +21,10 @@ class DataClient(Protocol):
         """
         Generic to load data
         """
+        ...
 
     def save_data(
-        self, obj: object, location: str, save_config: Union[dict, None]
+        self, obj: Any, location: str, save_config: Union[dict, None]
     ) -> None:
         """
         Generic to save data

modules/ml-pipeline/src/pipeline/core/interface/InterfaceModels.py

@@ -32,7 +32,9 @@ class MLModel(Protocol):
         """
         ...
 
-    def predict(self, data: pd.DataFrame) -> pd.Series:
+    def predict(
+        self, data: pd.DataFrame, post_prediction_logic: dict | None
+    ) -> pd.Series:
         """
         Method to predict
         """

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

@@ -0,0 +1,190 @@
+schema: '2.0'
+stages:
+  startup_cleanup:
+    cmd: python 0_startup_cleanup.py
+    deps:
+    - path: 0_startup_cleanup.py
+      hash: md5
+      md5: b1b12f6b6393fbf8b83d23684df0a3d4
+      size: 1220
+    params:
+      configs/settings.yaml:
+        default.startup_cleanup.artefacts: ./data
+        default.startup_cleanup.metrics: ./metrics
+  prepare_data:
+    cmd: python 1_prepare_data.py
+    deps:
+    - path: 1_prepare_data.py
+      hash: md5
+      md5: 11a3b8bfdfe199ab7ecc39ccc5652649
+      size: 4298
+    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
+        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: 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
+        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.train_proportion: 0.9
+    outs:
+    - path: data/prepared_data/
+      hash: md5
+      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      size: 45056059
+      nfiles: 2
+  build_model:
+    cmd: python 2_build_model.py
+    deps:
+    - path: 2_build_model.py
+      hash: md5
+      md5: 7231450b78920b0c5e7c6bada496b24a
+      size: 4820
+    - path: data/prepared_data
+      hash: md5
+      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      size: 45056059
+      nfiles: 2
+    params:
+      configs/build_model.yaml:
+        default:
+          build_model:
+            model_type: AutogluonAutoML
+            model_save_filepath: ./data/model/optimised/
+            fit_metrics_filepath: ./metrics/fit_metrics.json
+            fit_predictions_filepath: ./data/fit_predictions/predictions.parquet
+            SKLearnLinearRegression:
+            SKLearnSVMRegression:
+              kernel: linear
+            AutogluonAutoML:
+              output_filepath: ./data/model/allmodels/
+              problem_type: regression
+              eval_metric: mean_squared_error
+              time_limit: 1800
+              presets: medium_quality
+              excluded_model_types:
+              - RF
+              - CAT
+              - NN_TORCH
+              - KNN
+              - XT
+              infer_limit: 0.05
+              infer_limit_batch_size: 10000
+              ag_args_ensemble:
+                num_folds_parallel: 2
+    outs:
+    - path: data/fit_predictions/
+      hash: md5
+      md5: d9c9afc05e8780db47c0548b19bf7d19.dir
+      size: 3349989
+      nfiles: 1
+    - path: data/model/
+      hash: md5
+      md5: 13c3100e1486c27a83a8a47491077842.dir
+      size: 773523079
+      nfiles: 36
+    - path: metrics/fit_metrics.json
+      hash: md5
+      md5: 2ff70a2a45813e1bcdf2ea3aa8e07d4a
+      size: 224
+  generate_predictions:
+    cmd: python 3_generate_predictions.py
+    deps:
+    - path: 3_generate_predictions.py
+      hash: md5
+      md5: 0a70ad4dfe99414a75d1261c75a177b9
+      size: 2464
+    - path: data/model
+      hash: md5
+      md5: 13c3100e1486c27a83a8a47491077842.dir
+      size: 773523079
+      nfiles: 36
+    - path: data/prepared_data
+      hash: md5
+      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
+    outs:
+    - path: data/predictions/
+      hash: md5
+      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
+      size: 463197
+      nfiles: 1
+  generate_metrics:
+    cmd: python 4_generate_metrics.py
+    deps:
+    - path: 4_generate_metrics.py
+      hash: md5
+      md5: 4fedb86d89d528f0a6597934ba3890a0
+      size: 3484
+    - path: data/predictions
+      hash: md5
+      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
+      size: 463197
+      nfiles: 1
+    - path: data/prepared_data
+      hash: md5
+      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      size: 45056059
+      nfiles: 2
+    params:
+      configs/settings.yaml:
+        default.generate_metrics.dataclient_type: local
+        default.generate_metrics.metrics_output_filepath: ./metrics/metrics.json
+        default.generate_metrics.metrics_type: Regression
+    outs:
+    - path: metrics/metrics.json
+      hash: md5
+      md5: 3e08df02fd5c5d094bcf936e1338d596
+      size: 223
+  generate_scenerio_metrics:
+    cmd: python 5_generate_scenarios.py
+    deps:
+    - path: 5_generate_scenarios.py
+      hash: md5
+      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: fa4d6d7bbd7818613800da5f8f37ea96
+      size: 363
+    - path: metrics/scenario_table.md
+      hash: md5
+      md5: d6baf100a1623cc2467c2f8221d314c9
+      size: 2133

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

@@ -0,0 +1,87 @@
+stages:
+  startup_cleanup:
+    cmd: python 0_startup_cleanup.py
+    deps:
+    - 0_startup_cleanup.py
+    params:
+    - configs/settings.yaml:
+      - default.startup_cleanup.artefacts
+      - default.startup_cleanup.metrics
+    always_changed: true
+  prepare_data:
+    cmd: python 1_prepare_data.py
+    deps:
+    - 1_prepare_data.py
+    params:
+    - configs/settings.yaml:
+      - default.prepare_data.input_dataclient_type
+      - default.prepare_data.output_dataclient_type
+      - default.prepare_data.data_filepath
+      - default.prepare_data.train_proportion
+      - default.prepare_data.output_train_filepath
+      - default.prepare_data.output_test_filepath
+      - default.feature_processor.feature_processor_type
+      - default.feature_processor.feature_processor_config.subsample_amount
+      - default.feature_processor.feature_processor_config.subsample_seed
+      - default.feature_processor.feature_processor_config.target
+      - default.feature_processor.feature_processor_config.drop_columns
+      - default.feature_processor.feature_processor_config.retain_features
+    outs:
+    - data/prepared_data/
+    always_changed: true
+  build_model:
+    cmd: python 2_build_model.py
+    deps:
+    - 2_build_model.py
+    - data/prepared_data
+    params:
+    - configs/build_model.yaml:
+    outs:
+    - data/model/
+    - data/fit_predictions/
+    - metrics/fit_metrics.json
+    always_changed: true
+  generate_predictions:
+    cmd: python 3_generate_predictions.py
+    deps:
+    - 3_generate_predictions.py
+    - data/prepared_data
+    - data/model
+    params:
+    - configs/settings.yaml:
+      - default.generate_predictions.input_dataclient_type
+      - default.generate_predictions.output_dataclient_type
+      - default.generate_predictions.test_data_filepath
+      - default.generate_predictions.predictions_output_filepath
+      - default.generate_predictions.predictions_column_name
+    outs:
+    - data/predictions/
+    always_changed: true
+  generate_metrics:
+    cmd: python 4_generate_metrics.py
+    deps:
+    - 4_generate_metrics.py
+    - data/prepared_data
+    - data/predictions
+    params:
+    - configs/settings.yaml:
+      - default.generate_metrics.dataclient_type
+      - default.generate_metrics.metrics_type
+      - default.generate_metrics.metrics_output_filepath
+    outs:
+    - metrics/metrics.json
+    always_changed: true
+  generate_scenerio_metrics:
+    cmd: python 5_generate_scenarios.py
+    deps:
+    - 5_generate_scenarios.py
+    params:
+    - configs/scenarios.yaml:
+      - default.scenarios
+    outs:
+    - metrics/scenario_table.md
+    - metrics/scenario_metrics.md
+    always_changed: true
+metrics:
+  - metrics/metrics.json
+  - metrics/fit_metrics.json

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

@@ -0,0 +1,248 @@
+"""
+Doing some eda on dataset
+"""
+# Look at response variable
+
+from matplotlib import pyplot as plt
+import pandas as pd
+
+train_df = pd.read_parquet("./data/prepared_data/train.parquet")
+target = "SAP_ENDING"
+
+train_df = train_df.head(10000)
+
+# train_df[target].plot(kind='hist')
+
+# Plot the target variable
+fig, ax = plt.subplots(figsize=(10, 7))
+ax.hist(train_df[target], bins=range(min(train_df[target]), max(train_df[target])))
+
+fig
+
+# Find correlation to sale price (numeric)
+train_df.dtypes
+# All numerical
+
+train_df_corr = train_df.corr()
+
+train_df_corr.style.background_gradient(cmap="coolwarm")
+
+train_df_corr["EXTENSION_COUNT_ENDING"]
+
+# Check out some correlation plots between variables
+# sap starting - negative correlation
+
+train_df[[target, "SAP_STARTING"]].plot(y=target, x="SAP_STARTING", style="o")
+
+# head demand - light positive correlation
+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
+
+
+import os
+import yaml
+import pandas as pd
+from pathlib import Path
+from core.interface.InterfaceModels import MLModel
+from core.interface.InterfaceDataClient import DataClient
+from core.DataClient import dataclient_factory
+from core.MLModels import model_factory
+from core.Logger import logger
+
+
+RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
+
+client_path = Path(__file__).parent / "configs" / "client.yaml"
+client_params = yaml.safe_load(open(client_path))
+
+prepare_data_path = Path(__file__).parent / "configs" / "prepare_data.yaml"
+prepare_data_params = yaml.safe_load(open(prepare_data_path))
+
+build_model_path = Path(__file__).parent / "configs" / "build_model.yaml"
+build_model_params = yaml.safe_load(open(build_model_path))
+
+generate_predictions_path = (
+    Path(__file__).parent / "configs" / "generate_predictions.yaml"
+)
+generate_predictions_params = yaml.safe_load(open(generate_predictions_path))
+
+feature_process_path = Path(__file__).parent / "configs" / "feature_processor.yaml"
+feature_process_params = yaml.safe_load(open(feature_process_path))
+
+model = model_factory(build_model_params["model_type"])
+model_filepath = build_model_params["model_save_filepath"]
+
+model.load_model(model_filepath)
+
+fi = model.model.feature_importance(train_df.reset_index(drop=True))
+
+pred = pd.read_parquet("./data/predictions/predictions.parquet")
+test_df = pd.read_parquet("./data/prepared_data/test.parquet")
+
+# test_df = test_df.head(1000)
+
+test_df["predictions"] = pred["predictions"]
+
+test_df.groupby("PROPERTY_TYPE").apply(
+    lambda x: (x.SAP_ENDING - x.predictions).abs().mean()
+)
+
+test_df.head()
+flat_df = test_df[test_df["PROPERTY_TYPE"] == "Flat"]
+
+flat_df["residual"] = abs(flat_df["predictions"] - flat_df[target])
+
+generate_metrics_path = Path(__file__).parent / "configs" / "generate_metrics.yaml"
+generate_metrics_params = yaml.safe_load(open(generate_metrics_path))
+from core.MLMetrics import metrics_factory
+
+metrics = metrics_factory(generate_metrics_params["metrics_type"])
+
+metrics_output = metrics.generate_metrics(
+    target=flat_df[target],
+    predictions=pd.Series(flat_df["predictions"]),
+)
+
+# Use alibi to run permutation importance
+
+from alibi.explainers import PermutationImportance, plot_permutation_importance
+from sklearn.metrics import mean_absolute_percentage_error
+import numpy as np
+import pandas as pd
+
+test_df = pd.read_parquet("./data/prepared_data/test.parquet")
+test_df = test_df.head(1000)
+
+target = "SAP_ENDING"
+feature_names = test_df.columns.to_list()
+feature_names.remove(target)
+
+x = test_df[feature_names].to_numpy()
+y = test_df[target].to_numpy()
+
+
+def predict_fn(X: np.ndarray) -> np.ndarray:
+    return model.predict(pd.DataFrame(X, columns=feature_names))
+
+
+pfi = PermutationImportance(
+    predictor=predict_fn,
+    loss_fns=mean_absolute_percentage_error,
+    feature_names=feature_names,
+    verbose=True,
+)
+
+exp = pfi.explain(x, y)
+plot_permutation_importance(exp, fig_kw={"figwidth": 7, "figheight": 6})
+
+[
+    "PROPERTY_TYPE",
+    "BUILT_FORM",
+    "CONSTITUENCY",
+    "NUMBER_HABITABLE_ROOMS",
+    "NUMBER_HEATED_ROOMS",
+    "FIXED_LIGHTING_OUTLETS_COUNT",
+    "CONSTRUCTION_AGE_BAND",
+    "TRANSACTION_TYPE_STARTING",
+    "LIGHTING_DESCRIPTION_STARTING",
+    "MAINHEAT_DESCRIPTION_STARTING",
+    "HOTWATER_DESCRIPTION_STARTING",
+    "MAIN_FUEL_STARTING",
+    "MECHANICAL_VENTILATION_STARTING",
+    "SECONDHEAT_DESCRIPTION_STARTING",
+    "ENERGY_TARIFF_STARTING",
+    "SOLAR_WATER_HEATING_FLAG_STARTING",
+    "PHOTO_SUPPLY_STARTING",
+    "WINDOWS_DESCRIPTION_STARTING",
+    "GLAZED_TYPE_STARTING",
+    "MULTI_GLAZE_PROPORTION_STARTING",
+    "LOW_ENERGY_LIGHTING_STARTING",
+    "NUMBER_OPEN_FIREPLACES_STARTING",
+    "MAINHEATCONT_DESCRIPTION_STARTING",
+    "EXTENSION_COUNT_STARTING",
+    "TOTAL_FLOOR_AREA_STARTING",
+    "FLOOR_HEIGHT_STARTING",
+    "DAYS_TO_STARTING",
+    "WALLS_DESCRIPTION_STARTING",
+    "FLOOR_DESCRIPTION_STARTING",
+]
+
+# Use shap package to explain why 9158 has a 35 prediction when its sap ending is 96
+#
+#
+
+from core.MLModels import model_factory
+from core.DataClient import dataclient_factory
+import pandas as pd
+from config import settings
+
+client_params = settings.client
+prepare_data_params = settings.prepare_data
+feature_process_params = settings.feature_processor
+build_model_params = settings.build_model
+generate_predictions_params = settings.generate_predictions
+prediction_analysis_params = settings.prediction_analysis
+model = model_factory(build_model_params["model_type"])
+model.load_model(build_model_params["model_save_filepath"])
+dataclient_type = prediction_analysis_params["dataclient_type"]
+# dataclient_type = 'aws-s3'
+# dataclient = dataclient_factory(
+#     dataclient_type=dataclient_type,
+#     dataclient_config=client_params[dataclient_type],
+# )
+# data = dataclient.load_data("s3://retrofit-data-dev/sap_change_model/dataset.parquet")
+
+target = feature_process_params["feature_processor_config"]["target"]
+predictions_column_name = generate_predictions_params["predictions_column_name"]
+output_test_filepath = prepare_data_params["output_test_filepath"]
+predictions_output_filepath = generate_predictions_params["predictions_output_filepath"]
+
+# score_data = dataclient.load_data("s3://retrofit-data-dev/carbon_change_predictions/51/2023-11-28T21:01:21.869339.parquet")
+
+
+local_dataclient = dataclient_factory(
+    dataclient_type="local",
+    dataclient_config=client_params["local"],
+)
+test_df = local_dataclient.load_data(output_test_filepath)
+predictions = local_dataclient.load_data(predictions_output_filepath)
+mix_df = pd.concat([test_df.copy(), predictions], axis=1)
+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"])]
+from sklearn.metrics.pairwise import cosine_similarity
+
+row_index = 0
+
+from sklearn.preprocessing import LabelEncoder
+
+object_columns = cosine_similarity_df.select_dtypes(["object"])
+
+cosine_similarity_df[object_columns.columns] = cosine_similarity_df[
+    object_columns.columns
+].apply(LabelEncoder().fit_transform)
+
+feature_vector = cosine_similarity_df.loc[[row_index]]
+
+cosine_similarity_df["cosine"] = cosine_similarity(cosine_similarity_df, feature_vector)
+similar_index = (
+    cosine_similarity_df.sort_values("cosine", ascending=False).head(15).index
+)
+
+check_df = mix_df.loc[similar_index]
+
+columns_to_check = [
+    "LOW_ENERGY_LIGHTING_ENDING",
+    "walls_thermal_transmittance_ENDING",
+    "floor_thermal_transmittance_ENDING",
+    "roof_thermal_transmittance_ENDING",
+    "roof_insulation_thickness_ENDING",
+]
+
+cosine_similarity_df = mix_df[columns_to_check]

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

@@ -0,0 +1,56 @@
+import pandas as pd
+from configs.post_prediction_logic import post_prediction_logic
+from core.interface.InterfaceModels import MLModel
+from core.interface.InterfaceDataClient import DataClient
+from core.Logger import logger
+
+
+def generate_predictions(
+    input_dataclient: DataClient,
+    output_dataclient: DataClient,
+    model: MLModel,
+    target: str,
+    model_filepath: str,
+    test_data_filepath: str,
+    predictions_output_filepath: str,
+    predictions_column_name: str,
+    identifier_column: str = "id",
+):
+    """
+    For a given model, we generate prediction and evaluate this against the true target
+    """
+
+    logger.info("--- Loading test data ---")
+
+    test_data = input_dataclient.load_data(
+        location=test_data_filepath, load_config=None
+    )
+
+    logger.info("--- Loading model ---")
+
+    model.load_model(model_filepath)
+
+    logger.info("--- Generating predictions ---")
+
+    prediction_data = (
+        test_data.drop(columns=target) if target in test_data.columns else test_data
+    )
+
+    predictions = model.predict(
+        data=prediction_data, post_prediction_logic=post_prediction_logic
+    )
+
+    logger.info("--- Saving predictions ---")
+
+    predictions_df = pd.DataFrame(predictions)
+    predictions_df.columns = [predictions_column_name]
+
+    output_df = (
+        pd.concat([test_data[identifier_column], predictions_df], axis=1)
+        if identifier_column in test_data.columns
+        else predictions_df
+    )
+
+    output_dataclient.save_data(
+        obj=output_df, location=predictions_output_filepath, save_config=None
+    )

modules/ml-pipeline/src/pipeline/metrics/.gitignore

@@ -0,0 +1,4 @@
+/fit_metrics.json
+/metrics.json
+/scenario_table.md
+/scenario_metrics.md

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

@@ -0,0 +1,137 @@
+"""
+Post Model generation step:
+We want to look at feature analysis of the model
+"""
+
+from core.interface.InterfaceModels import MLModel
+from core.interface.InterfaceDataClient import DataClient
+from core.Logger import logger
+from core.MLModels import model_factory
+from core.DataClient import dataclient_factory
+from alibi.explainers import PermutationImportance, plot_permutation_importance
+import numpy as np
+import pandas as pd
+from config import settings
+
+
+client_params = settings.client
+prepare_data_params = settings.prepare_data
+feature_process_params = settings.feature_processor
+build_model_params = settings.build_model
+generate_predictions_params = settings.generate_predictions
+
+model_analysis_params = settings.model_analysis
+
+model = model_factory(build_model_params["model_type"])
+model.load_model(build_model_params["model_save_filepath"])
+
+dataclient_type = model_analysis_params["dataclient_type"]
+dataclient = dataclient_factory(
+    dataclient_type=dataclient_type,
+    dataclient_config=client_params[dataclient_type],
+)
+
+
+feature_importance_filepath = model_analysis_params["feature_importance_filepath"]
+permutation_subsample_amount = model_analysis_params["permutation_subsample_amount"]
+loss_fns = model_analysis_params["loss_fns"]
+feature_importance_column = model_analysis_params["feature_importance_column"]
+n_repeats = model_analysis_params["n_repeats"]
+figwidth = model_analysis_params["figwidth"]
+figheight = model_analysis_params["figheight"]
+target = feature_process_params["feature_processor_config"]["target"]
+output_test_filepath = prepare_data_params["output_test_filepath"]
+
+
+def model_analysis(
+    model: MLModel,
+    dataclient: DataClient,
+    target: str,
+    output_test_filepath: str,
+    feature_importance_filepath: str,
+    permutation_subsample_amount: int = 100,
+    loss_fns: str = "mean_absolute_percentage_error",
+    feature_importance_column: str = "importance",
+    n_repeats: int = 5,
+    figwidth: int = 7,
+    figheight: int = 6,
+):
+    """
+    Key task is to take in a model and generate:
+    - feature importance
+    and save these outputs
+    """
+
+    logger.info("------------------------------------")
+    logger.info(f"--- Generate Feature Importance ---")
+    logger.info("------------------------------------")
+
+    test_df = dataclient.load_data(output_test_filepath)
+
+    test_df = test_df.head(permutation_subsample_amount)
+
+    feature_names = test_df.columns.to_list()
+    feature_names.remove(target)
+
+    x = test_df[feature_names].to_numpy()
+    y = test_df[target].to_numpy()
+
+    def predict_fn(X: np.ndarray) -> np.ndarray:
+        return model.predict(pd.DataFrame(X, columns=feature_names))
+
+    pfi = PermutationImportance(
+        predictor=predict_fn,
+        loss_fns=loss_fns,
+        feature_names=feature_names,
+        verbose=True,
+    )
+
+    logger.info(
+        f"Permutation feature importance - using {permutation_subsample_amount} samples and {n_repeats} shuffles per feature:"
+    )
+
+    exp = pfi.explain(x, y, n_repeats=n_repeats)
+
+    mean_value_feature_importance = [
+        element["mean"] for element in exp.data["feature_importance"][0]
+    ]
+    feature_importance_df = pd.DataFrame(
+        mean_value_feature_importance,
+        index=exp.data["feature_names"],
+        columns=[feature_importance_column],
+    ).sort_values(feature_importance_column, ascending=False)
+
+    plot_permutation_importance(
+        exp, fig_kw={"figwidth": figwidth, "figheight": figheight}
+    )
+
+    logger.info("--------------------------------------")
+    logger.info(f"--- Save Feature Importance table ---")
+    logger.info("--------------------------------------")
+
+    dataclient.save_data(feature_importance_df, location=feature_importance_filepath)
+
+
+if __name__ == "__main__":
+
+    logger.info("----------------------------")
+    logger.info(f"--- {__file__} - Start! ---")
+    logger.info("----------------------------")
+
+    model_analysis(
+        model=model,
+        dataclient=dataclient,
+        target=target,
+        output_test_filepath=output_test_filepath,
+        feature_importance_filepath=feature_importance_filepath,
+        permutation_subsample_amount=permutation_subsample_amount,
+        loss_fns=loss_fns,
+        feature_importance_column=feature_importance_column,
+        n_repeats=n_repeats,
+        figwidth=figwidth,
+        figheight=figheight,
+    )
+
+    logger.info("-------------------------------")
+    logger.info(f"--- {__file__} - Complete! ---")
+    logger.info("-------------------------------")

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

@@ -0,0 +1,134 @@
+"""
+Look at why the model made such a prediction
+Manual script to run
+Workflow:
+- Identify a prediction row/s that you wish to look into
+    - i.e. a bad prediction/s
+- Add these rows to the config
+- Run script
+"""
+
+import shap
+
+shap.initjs()
+
+from typing import List
+from core.interface.InterfaceModels import MLModel
+from core.interface.InterfaceDataClient import DataClient
+from core.Logger import logger
+from core.MLModels import model_factory
+from core.DataClient import dataclient_factory
+import pandas as pd
+from config import settings
+
+client_params = settings.client
+prepare_data_params = settings.prepare_data
+feature_process_params = settings.feature_processor
+build_model_params = settings.build_model
+generate_predictions_params = settings.generate_predictions
+prediction_analysis_params = settings.prediction_analysis
+
+model = model_factory(build_model_params["model_type"])
+model.load_model(build_model_params["model_save_filepath"])
+
+dataclient_type = prediction_analysis_params["dataclient_type"]
+dataclient = dataclient_factory(
+    dataclient_type=dataclient_type,
+    dataclient_config=client_params[dataclient_type],
+)
+
+target = feature_process_params["feature_processor_config"]["target"]
+predictions_column_name = generate_predictions_params["predictions_column_name"]
+
+output_test_filepath = prepare_data_params["output_test_filepath"]
+
+predictions_output_filepath = generate_predictions_params["predictions_output_filepath"]
+
+nshap_samples = prediction_analysis_params["nshap_samples"]
+row_index = prediction_analysis_params["row_index"]
+
+
+def prediction_analysis(
+    model: MLModel,
+    dataclient: DataClient,
+    target: str,
+    predictions_column_name: str,
+    output_test_filepath: str,
+    predictions_output_filepath: str,
+    nshap_samples: int,
+    row_index: List[int],
+):
+
+    test_df = dataclient.load_data(output_test_filepath)
+    predictions = dataclient.load_data(predictions_output_filepath)
+
+    mix_df = pd.concat([test_df.copy(), predictions], axis=1)
+    mix_df["residual"] = abs(mix_df[predictions_column_name] - mix_df[target])
+    mix_df = mix_df.sort_values("residual", ascending=False)
+
+    test_df_without_target = test_df.drop(columns=[target])
+
+    class ModelWrapper:
+        def __init__(self, model, feature_names):
+            self.model = model
+            self.feature_names = feature_names
+
+        def predict(self, X):
+            if isinstance(X, pd.Series):
+                X = X.values.reshape(1, -1)
+            if not isinstance(X, pd.DataFrame):
+                X = pd.DataFrame(X, columns=self.feature_names)
+            return self.model.predict(X)
+
+    model_wrapper = ModelWrapper(model, feature_names=test_df_without_target.columns)
+
+    explainer = shap.KernelExplainer(
+        model_wrapper.predict, test_df_without_target.head(100)
+    )
+
+    shap_predictions_df = pd.DataFrame(index=test_df_without_target.columns)
+    for index in row_index:
+        single_datapoint = test_df_without_target.iloc[[index]]
+        # single_prediction = model_wrapper.predict(single_datapoint)
+        shap_values_single = explainer.shap_values(
+            single_datapoint, nsamples=nshap_samples
+        )
+        shap.force_plot(
+            explainer.expected_value,
+            shap_values_single,
+            test_df_without_target.iloc[index, :],
+        )
+        shap_single_prediction_df = pd.DataFrame(
+            shap_values_single, columns=test_df_without_target.columns
+        ).T
+        shap_single_prediction_df.columns = [index]
+        shap_single_prediction_df = shap_single_prediction_df.sort_values(index)
+        shap_predictions_df = pd.merge(
+            left=shap_predictions_df,
+            right=shap_single_prediction_df,
+            left_index=True,
+            right_index=True,
+        )
+
+    return shap_predictions_df
+
+
+if __name__ == "__main__":
+
+    logger.info("----------------------------")
+    logger.info(f"--- {__file__} - Start! ---")
+    logger.info("----------------------------")
+
+    prediction_analysis(
+        model=model,
+        dataclient=dataclient,
+        target=target,
+        predictions_column_name=predictions_column_name,
+        output_test_filepath=output_test_filepath,
+        nshap_samples=nshap_samples,
+        row_index=row_index,
+    )
+
+    logger.info("-------------------------------")
+    logger.info(f"--- {__file__} - Complete! ---")
+    logger.info("-------------------------------")

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

@@ -0,0 +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

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

@@ -0,0 +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

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

@@ -0,0 +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

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

@@ -0,0 +1,4 @@
+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

@@ -0,0 +1,4 @@
+dvc==3.51.0
+dvc-s3==3.2.0
+gto==1.7.1
+pyOpenSSL==23.3.0

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

@@ -1,3 +0,0 @@
-/config.local
-/tmp
-/cache

modules/ml-pipeline/src/pipeline/src/.dvcignore

@@ -1,3 +0,0 @@
-# Add patterns of files dvc should ignore, which could improve
-# the performance. Learn more at
-# https://dvc.org/doc/user-guide/dvcignore

modules/ml-pipeline/src/pipeline/src/.vscode/settings.json

@@ -1 +0,0 @@
-{}

modules/ml-pipeline/src/pipeline/src/README.md

@@ -1,35 +0,0 @@
-# Training
-
-This folder contains the code base for training experimentation.
-
-To understand the pipeline, run `dvc dag`
-
-There are 3 main steps:
-- Preparing data
-    - This is loading data (locally or from s3)
-    - Splitting the data into train and validation
-    - Creating additional features (if needed)
-    - **Data is cached**
-        - This will be down to the dvc remote location
-- Build model
-    - For the prepared data, we build a model using our configurations
-    - Model is saved (locally or s3)
-    - **Model is cached**
-        - This will be down to the dvc remote location
-- Generate Metrics
-    - For the given model, we generate metrics on validation data/test data
-    - **Metrics are cached**
-        - This will be down to the dvc remote location
-
-Workflow:
-- Use `dvc metrics show` to view current metrics score
-- Adjust parameters/ codebase
-- When happy with changes, use `dvc exp run` to trigger an experiment
-- Due to cache, only need stages are re-run
-- Use `dvc metrics diff` to check the change in metrics
-- Use `dvc exp show` to view all experiments
-    - NOTE: the last experiment will always be applied to the workspace!
-- After running experiments, you can apply the the best model to workspace using `dvc exp apply [EXPERIMENT_NAME]`
-    - This experiment will have the corresponding .dvc files for the hashed model and data
-- Use version control as normal
-    - git add, git commit etc

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

@@ -1,106 +0,0 @@
-"""
-Second Pipieline step:
-Once we have the features, we build a model
-"""
-
-import os
-import yaml
-import pandas as pd
-from typing import Union
-from pathlib import Path
-from core.Logger import logger
-from core.interface.InterfaceModels import MLModel
-from core.interface.InterfaceDataClient import DataClient
-from core.DataClient import dataclient_factory
-from core.MLModels import model_factory
-
-RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
-
-prepare_data_path = Path(__file__).parent / "configs" / "prepare_data.yaml"
-prepare_data_params = yaml.safe_load(open(prepare_data_path))
-
-build_model_path = Path(__file__).parent / "configs" / "build_model.yaml"
-build_model_params = yaml.safe_load(open(build_model_path))
-
-feature_process_path = Path(__file__).parent / "configs" / "feature_processor.yaml"
-feature_process_params = yaml.safe_load(open(feature_process_path))
-
-
-def build_model(
-    dataclient: DataClient,
-    model: MLModel,
-    target: str,
-    model_save_location: str,
-    model_hyperparameters: dict,
-    train_filepath: Union[str, None] = None,
-    test_filepath: Union[str, None] = None,
-    train_data: Union[pd.DataFrame, None] = None,
-    test_data: Union[pd.DataFrame, None] = None,
-    pipeline_mode: bool = False,
-):
-    logger.info("--------------------------------------")
-    logger.info("--- Loading Data for build process ---")
-    logger.info("--------------------------------------")
-
-    if train_data is None:
-        if train_filepath is None:
-            raise ValueError(f"Need {train_filepath} if no data supplied")
-        train_data = dataclient.load_data(location=train_filepath)
-
-    if test_data is None:
-        if test_filepath is None:
-            raise ValueError(f"Need {test_filepath} if no data supplied")
-        test_data = dataclient.load_data(location=test_filepath)
-
-    logger.info("----------------------")
-    logger.info("--- Training model ---")
-    logger.info("----------------------")
-
-    model.train_model(
-        data=train_data, target=target, model_hyperparameters=model_hyperparameters
-    )
-
-    logger.info("--------------------")
-    logger.info("--- Saving model ---")
-    logger.info("--------------------")
-
-    model.save_model(path=Path(model_save_location))
-
-
-if __name__ == "__main__":
-
-    logger.info("----------------------------")
-    logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
-
-    logger.info("----------------------------")
-    logger.info(f"--- Initiate DataClient ---")
-    logger.info("----------------------------")
-
-    # Output of previous prepare data step, will be where the data is
-    dataclient = dataclient_factory(prepare_data_params["output_dataclient_type"])
-
-    logger.info("-------------------------")
-    logger.info(f"--- Initiate MLModel ---")
-    logger.info("-------------------------")
-
-    model_type = build_model_params["model_type"]
-    model = model_factory(model_type)
-
-    logger.info("--------------------------")
-    logger.info(f"--- Build Model Stage ---")
-    logger.info("--------------------------")
-
-    build_model(
-        dataclient=dataclient,
-        model=model,
-        target=feature_process_params["feature_processor_config"]["target"],
-        model_save_location=build_model_params["model_save_filepath"],
-        model_hyperparameters=build_model_params[model_type],
-        train_filepath=prepare_data_params["output_train_filepath"],
-        test_filepath=prepare_data_params["output_test_filepath"],
-    )
-
-    logger.info("-------------------------------")
-    logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

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

@@ -1,15 +0,0 @@
-model_type: SKLearnLinearRegression
-model_save_filepath: ./data/model/model.joblib
-
-SKLearnLinearRegression: null
-
-SKLearnSVMRegression:
-  kernel: "linear"
-
-AutogluonAutoML:
-  output_filepath: ./data/model/autogluonmodel/
-  problem_type: regression
-  eval_metric: mean_absolute_error
-  time_limit: 400
-  presets: high_quality
-  excluded_model_types: ['KNN']

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

@@ -1,10 +0,0 @@
-aws-s3:
-  AWS_ACCESS_KEY_ID: null
-  AWS_SECRET_ACCESS_KEY: null
-  ENDPOINT_URL: null
-aws-s3-mock:
-  AWS_ACCESS_KEY_ID: minio
-  AWS_SECRET_ACCESS_KEY: minio123
-  ENDPOINT_URL: http://localhost:9000
-local:
-  null

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

@@ -1,3 +0,0 @@
-"""
-Stitch all yaml configuration files together, override some settings (such as bucket location) based off environment variables
-"""

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

@@ -1,8 +0,0 @@
-feature_processor_type: dataframe
-feature_processor_config:
-  subsample_amount: null
-  subsample_seed: 0
-  target: RDSAP_CHANGE
-  drop_columns: ["UPRN", "HEAT_DEMAND_CHANGE", "CARBON_CHANGE"]
-  retain_features: ["TOTAL_FLOOR_AREA_STARTING", "SAP_STARTING", "HEAT_DEMAND_STARTING", "CARBON_STARTING", "NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS", "FIXED_LIGHTING_OUTLETS_COUNT", "PHOTO_SUPPLY_STARTING", "MULTI_GLAZE_PROPORTION_STARTING", "LOW_ENERGY_LIGHTING_STARTING", "NUMBER_OPEN_FIREPLACES_STARTING", "EXTENSION_COUNT_STARTING", "FLOOR_HEIGHT_STARTING", "PHOTO_SUPPLY_ENDING", "MULTI_GLAZE_PROPORTION_ENDING", "LOW_ENERGY_LIGHTING_ENDING", "NUMBER_OPEN_FIREPLACES_ENDING", "EXTENSION_COUNT_ENDING", "TOTAL_FLOOR_AREA_ENDING", "FLOOR_HEIGHT_ENDING", "DAYS_TO_STARTING", "DAYS_TO_ENDING"]
-  # retain_features: null

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

@@ -1,13 +0,0 @@
-"""
-During the feature processor step, we can apply additional business logic and feature generation by defining them here
-"""
-
-"""
-Business Logic dict + functions
-"""
-business_logic = {}
-
-"""
-New features dict + function
-"""
-new_feature_funcs = {}

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

@@ -1,5 +0,0 @@
-dataclient_type: local
-input_datahandler_type: parquet
-output_datahandler_type: json
-metrics_type: Regression
-metrics_output_filepath: ./metrics/metrics.json

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

@@ -1,5 +0,0 @@
-input_dataclient_type: local
-output_dataclient_type: local
-test_data_filepath: ./data/prepared_data/test.parquet
-predictions_output_filepath: ./data/predictions/predictions.parquet
-predictions_column_name: predictions

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

@@ -1,9 +0,0 @@
-input_dataclient_type: aws-s3
-output_dataclient_type: local
-datahandler_type: parquet
-data_filepath: s3://retrofit-data-dev/sap_change_model/dataset.parquet
-train_proportion: 0.9
-output_train_filepath: ./data/prepared_data/train.parquet
-output_test_filepath: ./data/prepared_data/test.parquet
-
-# cache_o

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

@@ -1,2 +0,0 @@
-artefacts: ./data
-metrics: ./metrics

modules/ml-pipeline/src/pipeline/src/data/.gitignore

@@ -1,3 +0,0 @@
-/prepared_data
-/model
-/predictions

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

@@ -1,123 +0,0 @@
-schema: '2.0'
-stages:
-  prepare_data:
-    cmd: python prepare_data.py
-    deps:
-    - path: prepare_data.py
-      hash: md5
-      md5: 2cfe9e3012280e0cecdb84da12c974d9
-      size: 5009
-    params:
-      configs/prepare_data.yaml:
-        output_test_filepath: ./data/prepared_data/test.parquet
-        output_train_filepath: ./data/prepared_data/train.parquet
-        train_proportion: 0.9
-    outs:
-    - path: data/prepared_data/
-      hash: md5
-      md5: ea0a2baf3931e692d6344ba609331089.dir
-      size: 13232732
-      nfiles: 2
-  build_model:
-    cmd: python build_model.py
-    deps:
-    - path: build_model.py
-      hash: md5
-      md5: 46bcc34f20c6851cd987640889eefde6
-      size: 3671
-    - path: data/prepared_data
-      hash: md5
-      md5: ea0a2baf3931e692d6344ba609331089.dir
-      size: 13232732
-      nfiles: 2
-    params:
-      configs/build_model.yaml:
-        AutogluonAutoML:
-          output_filepath: ./data/model/autogluonmodel/
-          problem_type: regression
-          eval_metric: mean_absolute_error
-          time_limit: 400
-          presets: high_quality
-          excluded_model_types:
-          - KNN
-        SKLearnLinearRegression:
-        SKLearnSVMRegression:
-          kernel: linear
-        model_save_filepath: ./data/model/model.joblib
-        model_type: SKLearnLinearRegression
-    outs:
-    - path: data/model/
-      hash: md5
-      md5: eb2b910dec66481e75bb6058622f6e55.dir
-      size: 1832
-      nfiles: 1
-  generate_predictions:
-    cmd: python generate_predictions.py
-    deps:
-    - path: data/model
-      hash: md5
-      md5: eb2b910dec66481e75bb6058622f6e55.dir
-      size: 1832
-      nfiles: 1
-    - path: data/prepared_data
-      hash: md5
-      md5: ea0a2baf3931e692d6344ba609331089.dir
-      size: 13232732
-      nfiles: 2
-    - path: generate_predictions.py
-      hash: md5
-      md5: d412c8c9b48b59a29f569633280a6e7f
-      size: 4237
-    params:
-      configs/generate_predictions.yaml:
-        input_dataclient_type: local
-        output_dataclient_type: local
-        predictions_column_name: predictions
-        predictions_output_filepath: ./data/predictions/predictions.parquet
-        test_data_filepath: ./data/prepared_data/test.parquet
-    outs:
-    - path: data/predictions/
-      hash: md5
-      md5: 85ec3fa0cb387a7775eccd23185f7966.dir
-      size: 643406
-      nfiles: 1
-  generate_metrics:
-    cmd: python generate_metrics.py
-    deps:
-    - path: data/predictions
-      hash: md5
-      md5: 85ec3fa0cb387a7775eccd23185f7966.dir
-      size: 643406
-      nfiles: 1
-    - path: data/prepared_data
-      hash: md5
-      md5: ea0a2baf3931e692d6344ba609331089.dir
-      size: 13232732
-      nfiles: 2
-    - path: generate_metrics.py
-      hash: md5
-      md5: 5577a28107458dc1e6bcaaa098388095
-      size: 4144
-    params:
-      configs/generate_metrics.yaml:
-        dataclient_type: local
-        input_datahandler_type: parquet
-        metrics_output_filepath: ./metrics/metrics.json
-        metrics_type: Regression
-        output_datahandler_type: json
-    outs:
-    - path: metrics/metrics.json
-      hash: md5
-      md5: d79f798a272e6b50597be4d08ae48fa8
-      size: 180
-  startup_cleanup:
-    cmd: python startup_cleanup.py
-    deps:
-    - path: startup_cleanup.py
-      hash: md5
-      md5: 2e51fbcac960d0f960bf32a8ec7486a0
-      size: 1748
-    params:
-      configs/startup_cleanup.yaml:
-        artefacts: ./data
-        metrics: ./metrics

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

@@ -1,56 +0,0 @@
-stages:
-  startup_cleanup:
-    cmd: python startup_cleanup.py
-    deps:
-    - startup_cleanup.py
-    params:
-    - configs/startup_cleanup.yaml:
-      - artefacts
-      - metrics
-    always_changed: true
-  prepare_data:
-    cmd: python prepare_data.py
-    deps:
-    - prepare_data.py
-    params:
-    - configs/prepare_data.yaml:
-      - output_test_filepath
-      - output_train_filepath
-      - train_proportion
-    outs:
-    - data/prepared_data/
-    always_changed: true
-  build_model:
-    cmd: python build_model.py
-    deps:
-    - build_model.py
-    - data/prepared_data
-    params:
-    - configs/build_model.yaml:
-    outs:
-    - data/model/
-    always_changed: true
-  generate_predictions:
-    cmd: python generate_predictions.py
-    deps:
-    - generate_predictions.py
-    - data/prepared_data
-    - data/model
-    params:
-    - configs/generate_predictions.yaml:
-    outs:
-    - data/predictions/
-    always_changed: true
-  generate_metrics:
-    cmd: python generate_metrics.py
-    deps:
-    - generate_metrics.py
-    - data/prepared_data
-    - data/predictions
-    params:
-    - configs/generate_metrics.yaml:
-    outs:
-    - metrics/metrics.json
-    always_changed: true
-metrics:
-  - metrics/metrics.json

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

@@ -1,120 +0,0 @@
-"""
-Third part of the pipeline:
-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
-from core.DataClient import dataclient_factory
-from core.MLModels import model_factory
-from core.MLMetrics import metrics_factory
-from core.Logger import logger
-
-
-RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
-
-client_path = Path(__file__).parent / "configs" / "client.yaml"
-client_params = yaml.safe_load(open(client_path))
-
-prepare_data_path = Path(__file__).parent / "configs" / "prepare_data.yaml"
-prepare_data_params = yaml.safe_load(open(prepare_data_path))
-
-build_model_path = Path(__file__).parent / "configs" / "build_model.yaml"
-build_model_params = yaml.safe_load(open(build_model_path))
-
-generate_predictions_path = (
-    Path(__file__).parent / "configs" / "generate_predictions.yaml"
-)
-generate_predictions_params = yaml.safe_load(open(generate_predictions_path))
-
-generate_metrics_path = Path(__file__).parent / "configs" / "generate_metrics.yaml"
-generate_metrics_params = yaml.safe_load(open(generate_metrics_path))
-
-feature_process_path = Path(__file__).parent / "configs" / "feature_processor.yaml"
-feature_process_params = yaml.safe_load(open(feature_process_path))
-
-
-def generate_metrics(
-    input_dataclient: DataClient,
-    output_dataclient: DataClient,
-    model: MLModel,
-    metrics: MLMetrics,
-    target: str,
-    test_data_filepath: str,
-    predictions_output_filepath: str,
-    predictions_column_name: str,
-    metrics_output_filepath: str,
-):
-    """
-    For a given model, we generate prediction and evaluate this against the true target
-    """
-
-    logger.info("-------------------------")
-    logger.info("--- Loading test data ---")
-    logger.info("-------------------------")
-
-    test_data = input_dataclient.load_data(
-        location=test_data_filepath,
-    )
-
-    logger.info("---------------------------")
-    logger.info("--- Loading predictions ---")
-    logger.info("---------------------------")
-
-    predictions = input_dataclient.load_data(location=predictions_output_filepath)
-
-    logger.info("--------------------------")
-    logger.info("--- Generating metrics ---")
-    logger.info("--------------------------")
-
-    metrics_output = metrics.generate_metrics(
-        target=test_data[target],
-        predictions=pd.Series(predictions[predictions_column_name]),
-    )
-
-    logger.info("----------------------")
-    logger.info("--- Saving metrics ---")
-    logger.info("----------------------")
-
-    output_dataclient.save_data(obj=metrics_output, location=metrics_output_filepath)
-
-
-if __name__ == "__main__":
-
-    logger.info("----------------------------")
-    logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
-
-    model = model_factory(build_model_params["model_type"])
-
-    # Use data client for input and output, as we use dvc to cache later to the cloud
-    dataclient_type = generate_metrics_params["dataclient_type"]
-    dataclient = dataclient_factory(
-        dataclient_type=dataclient_type,
-        dataclient_config=client_params[dataclient_type],
-    )
-
-    metrics = metrics_factory(generate_metrics_params["metrics_type"])
-
-    generate_metrics(
-        input_dataclient=dataclient,
-        output_dataclient=dataclient,
-        model=model,
-        metrics=metrics,
-        target=feature_process_params["feature_processor_config"]["target"],
-        test_data_filepath=generate_predictions_params["test_data_filepath"],
-        predictions_output_filepath=generate_predictions_params[
-            "predictions_output_filepath"
-        ],
-        predictions_column_name=generate_predictions_params["predictions_column_name"],
-        metrics_output_filepath=generate_metrics_params["metrics_output_filepath"],
-    )
-
-    logger.info("-------------------------------")
-    logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

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

@@ -1,123 +0,0 @@
-"""
-Third part of the pipeline:
-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.InterfaceDataClient import DataClient
-from core.DataClient import dataclient_factory
-from core.MLModels import model_factory
-from core.Logger import logger
-
-
-RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
-
-client_path = Path(__file__).parent / "configs" / "client.yaml"
-client_params = yaml.safe_load(open(client_path))
-
-prepare_data_path = Path(__file__).parent / "configs" / "prepare_data.yaml"
-prepare_data_params = yaml.safe_load(open(prepare_data_path))
-
-build_model_path = Path(__file__).parent / "configs" / "build_model.yaml"
-build_model_params = yaml.safe_load(open(build_model_path))
-
-generate_predictions_path = (
-    Path(__file__).parent / "configs" / "generate_predictions.yaml"
-)
-generate_predictions_params = yaml.safe_load(open(generate_predictions_path))
-
-feature_process_path = Path(__file__).parent / "configs" / "feature_processor.yaml"
-feature_process_params = yaml.safe_load(open(feature_process_path))
-
-
-def generate_predictions(
-    input_dataclient: DataClient,
-    output_dataclient: DataClient,
-    model: MLModel,
-    target: str,
-    model_filepath: str,
-    test_data_filepath: str,
-    predictions_output_filepath: str,
-    predictions_column_name: str,
-):
-    """
-    For a given model, we generate prediction and evaluate this against the true target
-    """
-
-    logger.info("-------------------------")
-    logger.info("--- Loading test data ---")
-    logger.info("-------------------------")
-
-    test_data = input_dataclient.load_data(location=test_data_filepath)
-
-    logger.info("---------------------")
-    logger.info("--- Loading model ---")
-    logger.info("---------------------")
-
-    model.load_model(model_filepath)
-
-    logger.info("------------------------------")
-    logger.info("--- Generating predictions ---")
-    logger.info("------------------------------")
-
-    prediction_data = (
-        test_data.drop(columns=target) if target in test_data.columns else test_data
-    )
-
-    predictions = model.predict(data=prediction_data)
-
-    logger.info("--------------------------")
-    logger.info("--- Saving predictions ---")
-    logger.info("--------------------------")
-
-    predictions_df = pd.DataFrame(predictions)
-    predictions_df.columns = [predictions_column_name]
-
-    output_dataclient.save_data(
-        obj=predictions_df, location=predictions_output_filepath
-    )
-
-
-if __name__ == "__main__":
-
-    logger.info("----------------------------")
-    logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
-
-    model = model_factory(build_model_params["model_type"])
-    # We may have different locations of loading hence why we use one specified in generate_predictions.yaml
-    # I.e. for metric runs, this will be a local data client
-    # For predictions, we will want a cloud data client
-
-    input_dataclient_type = generate_predictions_params["input_dataclient_type"]
-    input_dataclient = dataclient_factory(
-        dataclient_type=input_dataclient_type,
-        dataclient_config=client_params[input_dataclient_type],
-    )
-
-    output_dataclient_type = generate_predictions_params["output_dataclient_type"]
-    output_dataclient = dataclient_factory(
-        dataclient_type=output_dataclient_type,
-        dataclient_config=client_params[output_dataclient_type],
-    )
-
-    generate_predictions(
-        input_dataclient=input_dataclient,
-        output_dataclient=output_dataclient,
-        model=model,
-        target=feature_process_params["feature_processor_config"]["target"],
-        model_filepath=build_model_params["model_save_filepath"],
-        test_data_filepath=generate_predictions_params["test_data_filepath"],
-        predictions_output_filepath=generate_predictions_params[
-            "predictions_output_filepath"
-        ],
-        predictions_column_name=generate_predictions_params["predictions_column_name"],
-    )
-
-    logger.info("-------------------------------")
-    logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

modules/ml-pipeline/src/pipeline/src/metrics/.gitignore

@@ -1 +0,0 @@
-/metrics.json

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

@@ -1,8 +0,0 @@
-joblib==1.3.2
-boto3==1.28.17
-pandas==1.5.3
-autogluon==0.8.2
-pyarrow==13.0.0
-pre-commit==3.3.3
-sphinx==7.2.5
-sphinx_rtd_theme==1.3.0

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

@@ -1,6 +0,0 @@
-joblib==1.3.2
-boto3==1.28.17
-pandas==1.5.3
-autogluon==0.8.2
-pyarrow==13.0.0
-PyYAML==6.0.1

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

@@ -1,8 +0,0 @@
-joblib==1.3.2
-boto3==1.28.17
-pandas==1.5.3
-autogluon==0.8.2
-pyarrow==13.0.0
-pre-commit==3.3.3
-sphinx==7.2.5
-sphinx_rtd_theme==1.3.0

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

@@ -1,3 +0,0 @@
-boto3==1.28.41
-pandas==1.5.3
-autogluon==0.8.2

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

@@ -1,3 +0,0 @@
-dvc==3.18.0
-dvc-s3==2.23.0
-gto==1.0.4

modules/ml-pipeline/src/pipeline2/README.md

@@ -1,3 +0,0 @@
-# Pipeline2
-
-PLACEHOLDER PIPELINE IF NEEDED