Merge pull request #153 from Hestia-Homes/sap-dev-update-2

Sap dev update 2

.github/workflows/MLPipelinePullRequest.yml

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

README.md

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

deployment/Dockerfile.prediction.lambda

@@ -1,15 +1,16 @@
-FROM public.ecr.aws/lambda/python:3.10
+FROM public.ecr.aws/lambda/python:3.12
 
 # Set the working directory
 WORKDIR ${LAMBDA_TASK_ROOT}
-ENV PYTHONPATH "${PYTHONPATH}:${LAMBDA_TASK_ROOT}"
+ENV PYTHONPATH="${PYTHONPATH}:${LAMBDA_TASK_ROOT}"
+ENV MPLCONFIGDIR="${LAMBDA_TASK_ROOT}/tmp/matplotlib"
 
 # Environment variables
 ARG RUNTIME_ENVIRONMENT
 ENV RUNTIME_ENVIRONMENT=${RUNTIME_ENVIRONMENT}
 
 # Install necessary build tools - required to test locally
-RUN yum install -y gcc python3-devel gcc-c++
+RUN dnf install -y gcc python3-devel gcc-c++
 
 # Install python packages
 COPY modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt ./requirements.txt

deployment/handlers/prediction_app.py

@@ -47,6 +47,23 @@ def upload_dataframe_to_s3(df, bucket, s3_file_name):
         return False
 
 
+def warming_up_invocation(model_filepath: str):
+    """
+    Function to handle warm up invocations
+    """
+    import pandas as pd
+
+    model = model_factory(settings.build_model["model_type"])
+    model_filepath = settings.build_model["model_save_filepath"]
+    model.load_model(model_filepath)
+
+    warmup_df = pd.DataFrame(columns=model.model.original_features)
+    warmup_df = pd.concat([warmup_df.T, pd.DataFrame([0] * len(warmup_df.T))], axis=1).T
+    warmup_df.fillna(0, inplace=True)
+
+    model.predict(data=warmup_df)
+
+
 def handler(event, context):
     """
     Take in event and trigger the prediction pipeline
@@ -66,9 +83,6 @@ def handler(event, context):
         created_at = body["created_at"]
 
         # TODO: Implement the loading of the model and prediction
-
-        storage_filepath = f"s3://{PREDICTIONS_BUCKET}/{portfolio_id}/{property_id}/{created_at}.parquet"
-
         logger.info(f"--- Initiate MLModel ---")
 
         build_model_params = settings.build_model
@@ -78,6 +92,32 @@ def handler(event, context):
 
         model = model_factory(build_model_params["model_type"])
 
+        model_filepath = build_model_params["model_save_filepath"]
+
+        if "warm" in body:
+            logger.info("Warm up invocation - synthetic prediction")
+
+            warming_up_invocation(model_filepath=model_filepath)
+
+            return {
+                "statusCode": 200,
+                "body": json.dumps(
+                    {
+                        "message": "Successfully warmed up invocation",
+                    }
+                ),
+            }
+
+        if "testing" in body:
+            logger.info(
+                "Testing invocation for CI/CD - save file to same location in S3"
+            )
+            storage_filepath = body["file_location"].replace(
+                ".parquet", "_output.parquet"
+            )
+        else:
+            storage_filepath = f"s3://{PREDICTIONS_BUCKET}/{portfolio_id}/{property_id}/{created_at}.parquet"
+
         logger.info(f"--- Initiate Input DataClient ---")
         input_dataclient = dataclient_factory(
             dataclient_type="aws-s3",
@@ -95,7 +135,7 @@ def handler(event, context):
             output_dataclient=output_dataclient,
             model=model,
             target=feature_process_params["feature_processor_config"]["target"],
-            model_filepath=build_model_params["model_save_filepath"],
+            model_filepath=model_filepath,
             test_data_filepath=body["file_location"],
             predictions_output_filepath=storage_filepath,
             predictions_column_name=generate_predictions_params[

modules/ml-pipeline/Makefile

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

modules/ml-pipeline/src/Prediction.Dockerfile

@@ -1,5 +1,5 @@
 # Dockerfile that can be used to test loading a model to generate a prediction (part of CI/CD flow)
-FROM python:3.10.12-slim
+FROM python:3.12.12-slim
 
 RUN apt-get update && apt-get install -y libgomp1 gcc python3-dev
 

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

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

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

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

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

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

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

@@ -20,10 +20,13 @@ default:
     output_dataclient_type: local
     # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.parquet
     # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-10-03-22-57-23/dataset_rooms.parquet
-    data_filepath: s3://retrofit-data-dev/sap_change_model/2024-10-08-21-58-03/dataset_rooms.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-10-08-21-58-03/dataset_rooms.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/2025-09-05-14-05-32/dataset_rooms.parquet
+    data_filepath: s3://retrofit-data-dev/sap_change_model/2025-11-02-09-32-42/dataset_rooms.parquet
     train_proportion: 0.9
     output_train_filepath: ./data/prepared_data/train.parquet
     output_test_filepath: ./data/prepared_data/test.parquet
+    sample_test_filepath: ./data/prepared_data/sample_test.parquet
 
   feature_processor:
     feature_processor_type: dataframe
@@ -38,7 +41,7 @@ default:
         'number_habitable_rooms_starting', 'number_habitable_rooms_ending', 'number_heated_rooms_starting', 'number_heated_rooms_ending',
         'number_habitable_rooms', 'number_heated_rooms', 'lighting_cost_starting',
         'lighting_cost_ending', 'heating_cost_starting', 'heating_cost_ending', 'hot_water_cost_starting', 'hot_water_cost_ending',
-        'floor_thermal_transmittance', 'floor_thermal_transmittance_ending']
+        'floor_thermal_transmittance', 'floor_thermal_transmittance_ending', 'lodgement_date_starting', 'lodgement_date_ending',]
       retain_features: null
       # retain_features: ['uprn', 'sap_starting', 'hot_water_energy_eff_ending',
       #  'mainheat_energy_eff_ending', 'constituency', 'roof_energy_eff_ending',

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

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

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

@@ -16,8 +16,8 @@ stages:
     deps:
     - path: 1_prepare_data.py
       hash: md5
-      md5: 11a3b8bfdfe199ab7ecc39ccc5652649
+      md5: a5ce162e1c402c0f811a80ef78cf4dd5
-      size: 4298
+      size: 4481
     params:
       configs/settings.yaml:
         default.feature_processor.feature_processor_config.drop_columns:
@@ -42,24 +42,28 @@ stages:
         - hot_water_cost_ending
         - floor_thermal_transmittance
         - floor_thermal_transmittance_ending
+        - lodgement_date_starting
+        - lodgement_date_ending
         default.feature_processor.feature_processor_config.retain_features:
         default.feature_processor.feature_processor_config.subsample_amount:
         default.feature_processor.feature_processor_config.subsample_seed: 0
         default.feature_processor.feature_processor_config.target: sap_ending
         default.feature_processor.feature_processor_type: dataframe
         default.prepare_data.data_filepath:
-          s3://retrofit-data-dev/sap_change_model/2024-10-08-21-58-03/dataset_rooms.parquet
+          s3://retrofit-data-dev/sap_change_model/2025-11-02-09-32-42/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_test_filepath:
-        default.prepare_data.output_train_filepath: ./data/prepared_data/train.parquet
+          ./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: 9159a400187e6d65687b5e411a4cb0de.dir
+      md5: 54204b6a31ba369cfbd26b9b25bfa355.dir
-      size: 48034631
+      size: 46095230
-      nfiles: 2
+      nfiles: 3
   build_model:
     cmd: python 2_build_model.py
     deps:
@@ -69,9 +73,9 @@ stages:
       size: 4820
     - path: data/prepared_data
       hash: md5
-      md5: 9159a400187e6d65687b5e411a4cb0de.dir
+      md5: 54204b6a31ba369cfbd26b9b25bfa355.dir
-      size: 48034631
+      size: 46095230
-      nfiles: 2
+      nfiles: 3
     params:
       configs/build_model.yaml:
         default:
@@ -87,7 +91,7 @@ stages:
               output_filepath: ./data/model/allmodels/
               problem_type: regression
               eval_metric: mean_squared_error
-              time_limit: 1800
+              time_limit: 3600
               presets: medium_quality
               excluded_model_types:
               - RF
@@ -95,25 +99,93 @@ stages:
               - NN_TORCH
               - KNN
               - XT
-              infer_limit: 0.05
+              infer_limit: 1
               infer_limit_batch_size: 10000
+              fit_strategy: parallel
               ag_args_ensemble:
                 num_folds_parallel: 2
+              num_gpus: 0
+              hyperparameters:
+                NN_TORCH:
+                - {}
+                GBM:
+                - extra_trees: true
+                  ag_args:
+                    name_suffix: XT
+                - {}
+                - learning_rate: 0.03
+                  num_leaves: 128
+                  feature_fraction: 0.9
+                  min_data_in_leaf: 3
+                  ag_args:
+                    name_suffix: Large
+                    priority: 0
+                CAT:
+                - {}
+                XGB:
+                - {}
+                FASTAI:
+                - {}
+                RF:
+                - criterion: gini
+                  ag_args:
+                    name_suffix: Gini
+                    problem_types:
+                    - binary
+                    - multiclass
+                - criterion: entropy
+                  ag_args:
+                    name_suffix: Entr
+                    problem_types:
+                    - binary
+                    - multiclass
+                - criterion: squared_error
+                  ag_args:
+                    name_suffix: MSE
+                    problem_types:
+                    - regression
+                    - quantile
+                XT:
+                - criterion: gini
+                  ag_args:
+                    name_suffix: Gini
+                    problem_types:
+                    - binary
+                    - multiclass
+                - criterion: entropy
+                  ag_args:
+                    name_suffix: Entr
+                    problem_types:
+                    - binary
+                    - multiclass
+                - criterion: squared_error
+                  ag_args:
+                    name_suffix: MSE
+                    problem_types:
+                    - regression
+                    - quantile
+                KNN:
+                - weights: uniform
+                  ag_args:
+                    name_suffix: Unif
+                - weights: distance
+                  ag_args:
+                    name_suffix: Dist
     outs:
     - path: data/fit_predictions/
       hash: md5
-      md5: 6ac50c46e6fd740ccf76da4c2bf6735d.dir
+      md5: f29cfa6a2dadf4fbe81813b3d517fd10.dir
-      size: 3615441
+      size: 3474971
       nfiles: 1
     - path: data/model/
       hash: md5
-      md5: 2212643103819177f58da1d3063c8c94.dir
+      md5: 1156f526fe9d11134e49f805c41c3781.dir
-      size: 761489901
+      size: 763384978
       nfiles: 35
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: d379cf95e07eb7c8797b4b766f8292cf
+      md5: 24b2f7c34e5e08b66f39289afac5d795
-      size: 225
+      size: 226
   generate_predictions:
     cmd: python 3_generate_predictions.py
     deps:
@@ -123,26 +195,28 @@ stages:
       size: 2464
     - path: data/model
       hash: md5
-      md5: 2212643103819177f58da1d3063c8c94.dir
+      md5: 1156f526fe9d11134e49f805c41c3781.dir
-      size: 761489901
+      size: 763384978
       nfiles: 35
     - path: data/prepared_data
       hash: md5
-      md5: 9159a400187e6d65687b5e411a4cb0de.dir
+      md5: 54204b6a31ba369cfbd26b9b25bfa355.dir
-      size: 48034631
+      size: 46095230
-      nfiles: 2
+      nfiles: 3
     params:
       configs/settings.yaml:
         default.generate_predictions.input_dataclient_type: local
         default.generate_predictions.output_dataclient_type: local
         default.generate_predictions.predictions_column_name: predictions
-        default.generate_predictions.predictions_output_filepath: ./data/predictions/predictions.parquet
+        default.generate_predictions.predictions_output_filepath:
-        default.generate_predictions.test_data_filepath: ./data/prepared_data/test.parquet
+          ./data/predictions/predictions.parquet
+        default.generate_predictions.test_data_filepath:
+          ./data/prepared_data/test.parquet
     outs:
     - path: data/predictions/
       hash: md5
-      md5: e8bd8e8ba88a667ccea645890d348e62.dir
+      md5: e9b1d9b94d1e44c999c17b7a2d096db9.dir
-      size: 507948
+      size: 484818
       nfiles: 1
   generate_metrics:
     cmd: python 4_generate_metrics.py
@@ -153,14 +227,14 @@ stages:
       size: 3484
     - path: data/predictions
       hash: md5
-      md5: e8bd8e8ba88a667ccea645890d348e62.dir
+      md5: e9b1d9b94d1e44c999c17b7a2d096db9.dir
-      size: 507948
+      size: 484818
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: 9159a400187e6d65687b5e411a4cb0de.dir
+      md5: 54204b6a31ba369cfbd26b9b25bfa355.dir
-      size: 48034631
+      size: 46095230
-      nfiles: 2
+      nfiles: 3
     params:
       configs/settings.yaml:
         default.generate_metrics.dataclient_type: local
@@ -169,15 +243,15 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: a8cf405272776730f5818d50b20c6f43
+      md5: 88a4e49229cc3c329faf5bf0fcae3318
-      size: 222
+      size: 226
   generate_scenerio_metrics:
     cmd: python 5_generate_scenarios.py
     deps:
     - path: 5_generate_scenarios.py
       hash: md5
-      md5: 40506749fefd926d47c60ff5b16db307
+      md5: 872b0c762ce1c8933fcbc5f54d5d4b5d
-      size: 5337
+      size: 5658
     params:
       configs/scenarios.yaml:
         default.scenarios:
@@ -190,9 +264,9 @@ stages:
     outs:
     - path: metrics/scenario_metrics.md
       hash: md5
-      md5: a5d9c42d38ef50e4fdf99a3e6043af2a
+      md5: 3326cc2e59ac1671d99d3e1f27131f54
       size: 356
     - path: metrics/scenario_table.md
       hash: md5
-      md5: 3e48c953451af8852572299b66988910
+      md5: 0a434e055463ec9ade5de2de9bde7154
       size: 872

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

@@ -1,7 +1,7 @@
-joblib==1.3.2
+joblib==1.5.2
-boto3==1.28.17
+boto3==1.40.61
-pandas==2.1.4
+pandas==2.3.3
-autogluon.tabular[all]==1.0.0
+autogluon.tabular[all]==1.4.0
-dynaconf==3.2.1
+dynaconf==3.2.12
-pyarrow==13.0.0
+pyarrow==20.0.0
-pre-commit==3.3.3
+pre-commit==4.3.0

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

@@ -1,7 +1,7 @@
-joblib==1.3.2
+joblib==1.5.2
-boto3==1.28.17
+boto3==1.40.61
-pandas==2.1.4
+pandas==2.3.3
-autogluon.tabular[all]==1.0.0
+autogluon.tabular[all]==1.4.0
-dynaconf==3.2.1
+dynaconf==3.2.12
-pyarrow==13.0.0
+pyarrow==20.0.0
-PyYAML==6.0.1
+PyYAML==6.0.3

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

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

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

@@ -1,4 +1,4 @@
-boto3==1.28.41
+boto3==1.40.61
-pandas==2.1.4
+pandas==2.3.3
-autogluon.tabular[all]==1.0.0
+autogluon.tabular[all]==1.4.0
-dynaconf==3.2.1
+dynaconf==3.2.12