Merge pull request #65 from Hestia-Homes/sap-dev

Sap dev

.github/workflows/Deploy.yml

@@ -2,7 +2,7 @@ name: Sap Change Model Deploy
 
 on:
   push:
-    branches: [ sap_change-dev, sap_change-prod ]
+    branches: [ sap-dev, sap-prod ]
 
 jobs:
   deploy:
@@ -54,10 +54,12 @@ jobs:
       - name: Set stack_name
         id: set_stack_name
         run: |
-          if [[ "${{ github.ref_name }}" == "sap_change-dev" || "${{ github.ref_name }}" == "sap_change-prod" ]]; then
-            echo "::set-output name=stack_name::sapmodel"
-          else
+          # 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

.github/workflows/MLPipelinePostMerge.yml

@@ -10,7 +10,9 @@ on:
     types:
       - closed
     branches:
-      - "master"
+      - "sap-dev"
+      - "heat-dev"
+      - "carbon-dev"
 
 permissions: write-all
 
@@ -176,6 +178,8 @@ jobs:
         pip install -r modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
 
     - name: Register Model
+      env:
+        TARGET_BRANCH: ${{ github.base_ref }}
       run: |
 
         REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
@@ -203,11 +207,11 @@ jobs:
         git tag -a ${new_tag} -m "Assigning stage dev to artifact ${REGISTER_MODEL_NAME} version ${latest_version}"
         git push origin ${new_tag}
 
-        git checkout master
+        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}

.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"]
 
@@ -89,13 +89,14 @@ 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
         echo "## Model metrics" > report.md
 
         # Compare metrics to master
-        git fetch --depth=1 origin master:master
-        dvc metrics diff --md --all master >> report.md
+        git fetch --depth=1 origin ${TARGET_BRANCH}:${TARGET_BRANCH}
+        dvc metrics diff --md --all ${TARGET_BRANCH} >> report.md
 
         cml comment create report.md
 

MODEL_REGISTRY.md

@@ -8,17 +8,9 @@
         "active": true
     },
     "sap": {
-        "version": "v1.0.0",
+        "version": "v0.0.3",
         "stage": {
-            "dev": "v1.0.0"
-        },
-        "registered": true,
-        "active": true
-    },
-    "migrate": {
-        "version": null,
-        "stage": {
-            "dev": "f320b9e0e9f3ea7735aed1abee07b1fb498c39c3"
+            "dev": "v0.0.3"
         },
         "registered": true,
         "active": true

modules/ml-pipeline/src/README.md

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

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

@@ -6,7 +6,7 @@ Once we have the features, we build a model
 import os
 import yaml
 import pandas as pd
-from typing import Union
+from typing import Union, List
 from pathlib import Path
 from core.Logger import logger
 from core.interface.InterfaceMetrics import MLMetrics
@@ -31,6 +31,9 @@ generate_metrics_params = settings.generate_metrics
 
 model_type = build_model_params["model_type"]
 target = feature_process_params["feature_processor_config"]["target"]
+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"]
@@ -62,6 +65,7 @@ def build_model(
     model: MLModel,
     metrics: MLMetrics,
     target: str,
+    identifier_columns: List[str],
     model_save_location: str,
     model_hyperparameters: dict,
     fit_metrics_filepath: str,
@@ -90,17 +94,17 @@ def build_model(
     logger.info("----------------------")
 
     model.train_model(
-        data=train_data, target=target, model_hyperparameters=model_hyperparameters
+        data=train_data.drop(columns=identifier_columns),
+        target=target,
+        model_hyperparameters=model_hyperparameters,
     )
 
     logger.info("----------------------------------")
     logger.info("--- Generating fit predictions ---")
     logger.info("----------------------------------")
 
-    prediction_data = train_data.drop(columns=target)
-
     fit_predictions = model.predict(
-        data=prediction_data, post_prediction_logic=post_prediction_logic
+        data=train_data, post_prediction_logic=post_prediction_logic
     )
 
     logger.info("------------------------------")
@@ -142,6 +146,7 @@ if __name__ == "__main__":
         model=model,
         metrics=metrics,
         target=target,
+        identifier_columns=identifier_columns,
         model_save_location=model_save_location,
         model_hyperparameters=model_hyperparameters,
         train_filepath=train_filepath,

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

@@ -1,7 +1,7 @@
 default:
   build_model:
     model_type: AutogluonAutoML
-    model_save_filepath: ./data/model/autogluonmodel/
+    model_save_filepath: ./data/model/optimised/
     fit_metrics_filepath: ./metrics/fit_metrics.json
 
     SKLearnLinearRegression: null
@@ -10,9 +10,9 @@ default:
       kernel: "linear"
 
     AutogluonAutoML:
-      output_filepath: ./data/model/autogluonmodel/
+      output_filepath: ./data/model/allmodels/
       problem_type: regression
-      eval_metric: mean_absolute_error
-      time_limit: 1000
+      eval_metric: mean_squared_error #mean_absolute_error
+      time_limit: 4000
       presets: medium_quality
-      excluded_model_types: ['KNN']
+      excluded_model_types: ['KNN', 'RF']

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

@@ -27,7 +27,7 @@ def remove_starting_columns(df):
 #     return df
 
 business_logic = {
-    "remove_starting_columns": remove_starting_columns
+    # "remove_starting_columns": remove_starting_columns
     # "keep_ENDING_COLUMNS": keep_ending_columns
 }
 

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

@@ -18,7 +18,10 @@ default:
   prepare_data:
     input_dataclient_type: aws-s3
     output_dataclient_type: local
-    data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_without_differencing.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_with_differencing.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/floor_area_clean_test.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_without_differencing.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
@@ -29,7 +32,9 @@ default:
       subsample_amount: null
       subsample_seed: 0
       target: SAP_ENDING
-      drop_columns: ["UPRN", "HEAT_DEMAND_CHANGE", "CARBON_CHANGE", "RDSAP_CHANGE", "HEAT_DEMAND_ENDING", "CARBON_ENDING"]
+      identifier_columns: ["UPRN"]
+      drop_columns: ["HEAT_DEMAND_CHANGE", "CARBON_CHANGE", "RDSAP_CHANGE", "HEAT_DEMAND_ENDING", "CARBON_ENDING"]
+      # retain_features: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"]
       retain_features: null
 
   generate_predictions:

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

@@ -165,8 +165,12 @@ 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("Using AutoGluon Model - Model saving already occured")
+        logger.info(
+            "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)
 

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

@@ -10,7 +10,6 @@ stages:
     params:
       configs/settings.yaml:
         default.feature_processor.feature_processor_config.drop_columns:
-        - UPRN
         - HEAT_DEMAND_CHANGE
         - CARBON_CHANGE
         - RDSAP_CHANGE
@@ -21,7 +20,7 @@ stages:
         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/dataset_without_differencing.parquet
+        default.prepare_data.data_filepath: s3://retrofit-data-dev/sap_change_model/dataset.parquet
         default.prepare_data.input_dataclient_type: aws-s3
         default.prepare_data.output_dataclient_type: local
         default.prepare_data.output_test_filepath: ./data/prepared_data/test.parquet
@@ -30,65 +29,66 @@ stages:
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: f9ef7ad073b43b249b43faa75c62fe07.dir
-      size: 21115444
+      md5: 9ce5c45722da7fc40491b5a4d00daf9e.dir
+      size: 33881619
       nfiles: 2
   build_model:
     cmd: python 2_build_model.py
     deps:
     - path: 2_build_model.py
       hash: md5
-      md5: 039578b629d7cd204016e92cd079ea90
-      size: 5181
+      md5: 84699d208874c52accaff61c6af9bb0a
+      size: 5359
     - path: data/prepared_data
       hash: md5
-      md5: f9ef7ad073b43b249b43faa75c62fe07.dir
-      size: 21115444
+      md5: 9ce5c45722da7fc40491b5a4d00daf9e.dir
+      size: 33881619
       nfiles: 2
     params:
       configs/build_model.yaml:
         default:
           build_model:
             model_type: AutogluonAutoML
-            model_save_filepath: ./data/model/autogluonmodel/
+            model_save_filepath: ./data/model/optimised/
             fit_metrics_filepath: ./metrics/fit_metrics.json
             SKLearnLinearRegression:
             SKLearnSVMRegression:
               kernel: linear
             AutogluonAutoML:
-              output_filepath: ./data/model/autogluonmodel/
+              output_filepath: ./data/model/allmodels/
               problem_type: regression
-              eval_metric: mean_absolute_error
-              time_limit: 1000
+              eval_metric: mean_squared_error
+              time_limit: 4000
               presets: medium_quality
               excluded_model_types:
               - KNN
+              - RF
     outs:
     - path: data/model/
       hash: md5
-      md5: d073af40ba5c7c2d9b8064665062f51e.dir
-      size: 363710367
-      nfiles: 20
+      md5: 7bb5156243b4db39349e80a01ffecde4.dir
+      size: 473398662
+      nfiles: 27
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: dcd9ea03a2771077e1bd14018bb7fd18
-      size: 183
+      md5: 2bb16ac67de8778fbc08171d562b34d5
+      size: 184
   generate_predictions:
     cmd: python 3_generate_predictions.py
     deps:
     - path: 3_generate_predictions.py
       hash: md5
-      md5: 238b3fa9f3c6f3720e77c116857070ae
-      size: 4720
+      md5: 5ef2856a5a977304f1ec01f9b4205262
+      size: 3028
     - path: data/model
       hash: md5
-      md5: d073af40ba5c7c2d9b8064665062f51e.dir
-      size: 363710367
-      nfiles: 20
+      md5: 7bb5156243b4db39349e80a01ffecde4.dir
+      size: 473398662
+      nfiles: 27
     - path: data/prepared_data
       hash: md5
-      md5: f9ef7ad073b43b249b43faa75c62fe07.dir
-      size: 21115444
+      md5: 9ce5c45722da7fc40491b5a4d00daf9e.dir
+      size: 33881619
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -100,8 +100,8 @@ stages:
     outs:
     - path: data/predictions/
       hash: md5
-      md5: a2ecfae1e418fe9cb9fe044c148bbb37.dir
-      size: 381538
+      md5: 0bb3cf991906953def81c8204cdcfaf0.dir
+      size: 374532
       nfiles: 1
   generate_metrics:
     cmd: python 4_generate_metrics.py
@@ -112,13 +112,13 @@ stages:
       size: 4487
     - path: data/predictions
       hash: md5
-      md5: a2ecfae1e418fe9cb9fe044c148bbb37.dir
-      size: 381538
+      md5: 0bb3cf991906953def81c8204cdcfaf0.dir
+      size: 374532
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: f9ef7ad073b43b249b43faa75c62fe07.dir
-      size: 21115444
+      md5: 9ce5c45722da7fc40491b5a4d00daf9e.dir
+      size: 33881619
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -128,8 +128,8 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: ec02774fd01243fa4706189c60087ccf
-      size: 182
+      md5: 2e13ae67759a64261d03224f1c0d4bf4
+      size: 185
   startup_cleanup:
     cmd: python 0_startup_cleanup.py
     deps:

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

@@ -175,3 +175,57 @@ plot_permutation_importance(exp, fig_kw={"figwidth": 7, "figheight": 6})
 # 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 = 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"]
+
+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)
+
+cosine_similarity_df = mix_df[
+    mix_df.columns.difference(["predictions", "residual", "SAP_ENDING"])
+]
+from sklearn.metrics.pairwise import cosine_similarity
+
+row_index = 58199
+
+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(5).index
+)
+
+check_df = mix_df.loc[similar_index]

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

@@ -1,3 +1,4 @@
 dvc==3.18.0
 dvc-s3==2.23.0
 gto==1.0.4
+pyOpenSSL==23.2.0