diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..84abbe6 --- /dev/null +++ b/.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 diff --git a/.github/workflows/Deploy.yml b/.github/workflows/Deploy.yml index 6e34d36..265a324 100644 --- a/.github/workflows/Deploy.yml +++ b/.github/workflows/Deploy.yml @@ -19,8 +19,8 @@ jobs: - name: Install Serverless and plugins run: | - npm install -g serverless - npm install -g serverless-domain-manager + npm install -g serverless@^3.38.0 + npm install -g serverless-domain-manager@^7.3.8 - name: Install DVC run: | diff --git a/.github/workflows/MLPipelinePullRequest.yml b/.github/workflows/MLPipelinePullRequest.yml index cbc379d..451b0a8 100644 --- a/.github/workflows/MLPipelinePullRequest.yml +++ b/.github/workflows/MLPipelinePullRequest.yml @@ -98,6 +98,16 @@ jobs: git fetch --depth=1 origin ${TARGET_BRANCH}:${TARGET_BRANCH} 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 # echo "## Residuals plot from model" >> report.md diff --git a/MODEL_REGISTRY.md b/MODEL_REGISTRY.md index 1cffecf..2fea343 100644 --- a/MODEL_REGISTRY.md +++ b/MODEL_REGISTRY.md @@ -8,6 +8,14 @@ "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" @@ -15,20 +23,12 @@ "registered": true, "active": true }, - "heat": { - "version": "v0.4.0", + "carbon": { + "version": "v0.5.0", "stage": { "dev": "v0.5.0" }, "registered": true, "active": true - }, - "carbon": { - "version": "v0.4.0", - "stage": { - "dev": "v0.3.0" - }, - "registered": true, - "active": true } } diff --git a/deployment/.dockerignore b/deployment/.dockerignore index e01cbd5..c4103de 100644 --- a/deployment/.dockerignore +++ b/deployment/.dockerignore @@ -1,4 +1,9 @@ -modules/ml-pipeline/src/pipeline/data/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/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 diff --git a/modules/ml-pipeline/src/.dockerignore b/modules/ml-pipeline/src/.dockerignore index 14f71d7..bf48a5e 100644 --- a/modules/ml-pipeline/src/.dockerignore +++ b/modules/ml-pipeline/src/.dockerignore @@ -1,4 +1,8 @@ -pipeline/data/predictions* -pipeline/data/prepared_data/train.parquet* -pipeline/data/model/allmodels* -pipeline/metrics* +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 diff --git a/modules/ml-pipeline/src/Prediction.Dockerfile b/modules/ml-pipeline/src/Prediction.Dockerfile index a6fc539..e0a292c 100644 --- a/modules/ml-pipeline/src/Prediction.Dockerfile +++ b/modules/ml-pipeline/src/Prediction.Dockerfile @@ -1,7 +1,7 @@ # 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 -RUN apt-get update && apt-get install -y libgomp1 +RUN apt-get update && apt-get install -y libgomp1 gcc python3-dev COPY pipeline/requirements/predictions/requirements.txt requirements.txt diff --git a/modules/ml-pipeline/src/pipeline/5_generate_scenarios.py b/modules/ml-pipeline/src/pipeline/5_generate_scenarios.py new file mode 100644 index 0000000..6debe32 --- /dev/null +++ b/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! ---") diff --git a/modules/ml-pipeline/src/pipeline/README.md b/modules/ml-pipeline/src/pipeline/README.md index d47f864..d44e220 100644 --- a/modules/ml-pipeline/src/pipeline/README.md +++ b/modules/ml-pipeline/src/pipeline/README.md @@ -37,3 +37,4 @@ Workflow: - 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}` diff --git a/modules/ml-pipeline/src/pipeline/analysis/feature_importance.parquet b/modules/ml-pipeline/src/pipeline/analysis/feature_importance.parquet index 6960946..ec8b0d3 100644 Binary files a/modules/ml-pipeline/src/pipeline/analysis/feature_importance.parquet and b/modules/ml-pipeline/src/pipeline/analysis/feature_importance.parquet differ diff --git a/modules/ml-pipeline/src/pipeline/config.py b/modules/ml-pipeline/src/pipeline/config.py index 7a7366b..bac430c 100644 --- a/modules/ml-pipeline/src/pipeline/config.py +++ b/modules/ml-pipeline/src/pipeline/config.py @@ -7,6 +7,7 @@ settings = Dynaconf( "./configs/settings.yaml", "./configs/build_model.yaml", "./configs/analysis.yaml", + "./configs/scenarios.yaml", ], ) diff --git a/modules/ml-pipeline/src/pipeline/configs/build_model.yaml b/modules/ml-pipeline/src/pipeline/configs/build_model.yaml index fcec7f7..a36bfbc 100644 --- a/modules/ml-pipeline/src/pipeline/configs/build_model.yaml +++ b/modules/ml-pipeline/src/pipeline/configs/build_model.yaml @@ -14,8 +14,9 @@ default: output_filepath: ./data/model/allmodels/ problem_type: regression eval_metric: mean_squared_error #mean_absolute_error - time_limit: 4000 + time_limit: 1800 presets: medium_quality - excluded_model_types: ['RF', 'FASTAI', 'CAT', 'NN_TORCH', 'KNN', 'XT'] + 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} diff --git a/modules/ml-pipeline/src/pipeline/configs/scenarios.yaml b/modules/ml-pipeline/src/pipeline/configs/scenarios.yaml new file mode 100644 index 0000000..0d4ee07 --- /dev/null +++ b/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 diff --git a/modules/ml-pipeline/src/pipeline/configs/settings.yaml b/modules/ml-pipeline/src/pipeline/configs/settings.yaml index 6d91444..75006d7 100644 --- a/modules/ml-pipeline/src/pipeline/configs/settings.yaml +++ b/modules/ml-pipeline/src/pipeline/configs/settings.yaml @@ -18,8 +18,10 @@ default: prepare_data: input_dataclient_type: aws-s3 output_dataclient_type: local - # data_filepath: s3://retrofit-datalake-dev/dataset_with0perm_all.parquet - data_filepath: s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet + # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/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 @@ -37,6 +39,29 @@ default: 'number_habitable_rooms', 'number_heated_rooms'] # retain_features: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"] 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 diff --git a/modules/ml-pipeline/src/pipeline/core/DataClient.py b/modules/ml-pipeline/src/pipeline/core/DataClient.py index 53f4072..b38ca32 100644 --- a/modules/ml-pipeline/src/pipeline/core/DataClient.py +++ b/modules/ml-pipeline/src/pipeline/core/DataClient.py @@ -245,7 +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 } @@ -294,3 +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) diff --git a/modules/ml-pipeline/src/pipeline/core/MLModels.py b/modules/ml-pipeline/src/pipeline/core/MLModels.py index 4fc572a..257261d 100644 --- a/modules/ml-pipeline/src/pipeline/core/MLModels.py +++ b/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 } @@ -151,6 +151,7 @@ class AutogluonAutoML: "excluded_model_types", "infer_limit", "infer_limit_batch_size", + "ag_args_ensemble", ] def load_model(self, path: Union[Path, str]) -> None: @@ -207,6 +208,7 @@ class AutogluonAutoML: 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( diff --git a/modules/ml-pipeline/src/pipeline/dvc.lock b/modules/ml-pipeline/src/pipeline/dvc.lock index 04ab745..b0ef6e2 100644 --- a/modules/ml-pipeline/src/pipeline/dvc.lock +++ b/modules/ml-pipeline/src/pipeline/dvc.lock @@ -39,8 +39,8 @@ stages: default.feature_processor.feature_processor_config.subsample_seed: 0 default.feature_processor.feature_processor_config.target: heat_demand_ending default.feature_processor.feature_processor_type: dataframe - default.prepare_data.data_filepath: - s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet + default.prepare_data.data_filepath: + s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.parquet default.prepare_data.input_dataclient_type: aws-s3 default.prepare_data.output_dataclient_type: local default.prepare_data.output_test_filepath: ./data/prepared_data/test.parquet @@ -49,8 +49,8 @@ stages: outs: - path: data/prepared_data/ hash: md5 - md5: 4cec69f112537658f14eb3cb678f91e3.dir - size: 36889932 + md5: 13cd955d579de20efe743f82bc434c7e.dir + size: 37294025 nfiles: 2 build_model: cmd: python 2_build_model.py @@ -61,8 +61,8 @@ stages: size: 4820 - path: data/prepared_data hash: md5 - md5: 4cec69f112537658f14eb3cb678f91e3.dir - size: 36889932 + md5: 13cd955d579de20efe743f82bc434c7e.dir + size: 37294025 nfiles: 2 params: configs/build_model.yaml: @@ -79,32 +79,33 @@ stages: output_filepath: ./data/model/allmodels/ problem_type: regression eval_metric: mean_squared_error - time_limit: 4000 + time_limit: 1800 presets: medium_quality excluded_model_types: - RF - - FASTAI - 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: 7dda2f1dd257a6c5beaaa0b74eab6d5d.dir - size: 2901760 + md5: b9c9ca64ea6973c409c3a7b8f8ed0c3e.dir + size: 2902493 nfiles: 1 - path: data/model/ hash: md5 - md5: 741f8aed57383e860c535feb8b0adb71.dir - size: 752079341 - nfiles: 32 + md5: a9215bba342ed7ec3f97815dfef94e48.dir + size: 727501601 + nfiles: 36 - path: metrics/fit_metrics.json hash: md5 - md5: 8eaa72b08074f735a9e54de871edc6e6 - size: 221 + md5: 548a431d58cd4f5a3118235dec734372 + size: 219 generate_predictions: cmd: python 3_generate_predictions.py deps: @@ -114,13 +115,13 @@ stages: size: 2464 - path: data/model hash: md5 - md5: 741f8aed57383e860c535feb8b0adb71.dir - size: 752079341 - nfiles: 32 + md5: a9215bba342ed7ec3f97815dfef94e48.dir + size: 727501601 + nfiles: 36 - path: data/prepared_data hash: md5 - md5: 4cec69f112537658f14eb3cb678f91e3.dir - size: 36889932 + md5: 13cd955d579de20efe743f82bc434c7e.dir + size: 37294025 nfiles: 2 params: configs/settings.yaml: @@ -132,8 +133,8 @@ stages: outs: - path: data/predictions/ hash: md5 - md5: d842fe5350a3330c4c17e7e21c6359b2.dir - size: 380489 + md5: 484781d6b359e458a25e9ab728d6514d.dir + size: 380517 nfiles: 1 generate_metrics: cmd: python 4_generate_metrics.py @@ -144,13 +145,13 @@ stages: size: 3447 - path: data/predictions hash: md5 - md5: d842fe5350a3330c4c17e7e21c6359b2.dir - size: 380489 + md5: 484781d6b359e458a25e9ab728d6514d.dir + size: 380517 nfiles: 1 - path: data/prepared_data hash: md5 - md5: 4cec69f112537658f14eb3cb678f91e3.dir - size: 36889932 + md5: 13cd955d579de20efe743f82bc434c7e.dir + size: 37294025 nfiles: 2 params: configs/settings.yaml: @@ -160,5 +161,30 @@ stages: outs: - path: metrics/metrics.json hash: md5 - md5: 2632fa5d0a38763c177bf0466a670c8b + md5: 4d246765aff7c45079d02b4d8f7527f7 size: 220 + 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: d9fbb5c725258b82c465ddd9f86f9c16 + size: 377 + - path: metrics/scenario_table.md + hash: md5 + md5: 396d20b1a049d5f93fc38a409c4ca497 + size: 2133 diff --git a/modules/ml-pipeline/src/pipeline/dvc.yaml b/modules/ml-pipeline/src/pipeline/dvc.yaml index 58889cc..6026a83 100644 --- a/modules/ml-pipeline/src/pipeline/dvc.yaml +++ b/modules/ml-pipeline/src/pipeline/dvc.yaml @@ -71,6 +71,17 @@ stages: 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 diff --git a/modules/ml-pipeline/src/pipeline/metrics/.gitignore b/modules/ml-pipeline/src/pipeline/metrics/.gitignore index e6fbc8d..6427764 100644 --- a/modules/ml-pipeline/src/pipeline/metrics/.gitignore +++ b/modules/ml-pipeline/src/pipeline/metrics/.gitignore @@ -1,2 +1,4 @@ /fit_metrics.json /metrics.json +/scenario_table.md +/scenario_metrics.md diff --git a/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt b/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt index 734419a..4dc4c36 100644 --- a/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt +++ b/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt @@ -1,7 +1,7 @@ joblib==1.3.2 boto3==1.28.17 pandas==2.1.4 -autogluon==1.0.0 +autogluon.tabular[all]==1.0.0 dynaconf==3.2.1 pyarrow==13.0.0 pre-commit==3.3.3 diff --git a/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt b/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt index 937b000..35bdb05 100644 --- a/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt +++ b/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt @@ -1,7 +1,7 @@ joblib==1.3.2 boto3==1.28.17 pandas==2.1.4 -autogluon==1.0.0 +autogluon.tabular[all]==1.0.0 dynaconf==3.2.1 pyarrow==13.0.0 PyYAML==6.0.1 diff --git a/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt b/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt index fe06a4d..93a042e 100644 --- a/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt +++ b/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt @@ -1,7 +1,7 @@ joblib==1.3.2 boto3==1.28.17 pandas==2.1.4 -autogluon==1.0.0 +autogluon.tabular[all]==1.0.0 ray==2.6.3 dynaconf==3.2.1 alibi==0.9.5 diff --git a/modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt b/modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt index e4ba8f1..edeb764 100644 --- a/modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt +++ b/modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt @@ -1,4 +1,4 @@ boto3==1.28.41 pandas==2.1.4 -autogluon==1.0.0 -dynaconf==3.2.1 \ No newline at end of file +autogluon.tabular[all]==1.0.0 +dynaconf==3.2.1 diff --git a/modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt b/modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt index a2b9531..173550d 100644 --- a/modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt +++ b/modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt @@ -1,4 +1,4 @@ -dvc==3.36.0 -dvc-s3==3.0.1 -gto==1.6.1 +dvc==3.51.0 +dvc-s3==3.2.0 +gto==1.7.1 pyOpenSSL==23.3.0