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/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..9d2fa68 --- /dev/null +++ b/modules/ml-pipeline/src/pipeline/5_generate_scenarios.py @@ -0,0 +1,150 @@ +""" +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() + + # 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/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..2df0cb6 --- /dev/null +++ b/modules/ml-pipeline/src/pipeline/configs/scenarios.yaml @@ -0,0 +1,10 @@ +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 + 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 19b0a5b..f42b2be 100644 --- a/modules/ml-pipeline/src/pipeline/configs/settings.yaml +++ b/modules/ml-pipeline/src/pipeline/configs/settings.yaml @@ -18,13 +18,8 @@ default: prepare_data: input_dataclient_type: aws-s3 output_dataclient_type: local - # 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_test.parquet - data_filepath: s3://retrofit-data-dev/sap_change_model/dataset.parquet - # data_filepath: s3://retrofit-datalake-dev/dataset_with0perm_all.parquet - train_proportion: 1 + data_filepath: s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet + train_proportion: 0.9 output_train_filepath: ./data/prepared_data/train.parquet output_test_filepath: ./data/prepared_data/test.parquet @@ -35,9 +30,35 @@ default: 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"] - # retain_features: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"] + # 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 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 826e654..104dc83 100644 --- a/modules/ml-pipeline/src/pipeline/dvc.lock +++ b/modules/ml-pipeline/src/pipeline/dvc.lock @@ -1,5 +1,16 @@ 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: @@ -17,22 +28,69 @@ stages: - 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: + - 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 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/dataset.parquet + default.prepare_data.data_filepath: + s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/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: 1 + default.prepare_data.train_proportion: 0.9 outs: - path: data/prepared_data/ hash: md5 - md5: 3c77fa10cd1cd503eb4d2540394629f6.dir - size: 42626894 + md5: efa416abea618ae6220a0c3d597603cf.dir + size: 44750997 nfiles: 2 build_model: cmd: python 2_build_model.py @@ -43,8 +101,8 @@ stages: size: 4820 - path: data/prepared_data hash: md5 - md5: 3c77fa10cd1cd503eb4d2540394629f6.dir - size: 42626894 + md5: efa416abea618ae6220a0c3d597603cf.dir + size: 44750997 nfiles: 2 params: configs/build_model.yaml: @@ -61,32 +119,33 @@ 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: e0a11ac6e4adf69d6180c0217c639a0e.dir - size: 3680908 + md5: de46250d454c4d713ab580b10ff3fd31.dir + size: 3349318 nfiles: 1 - path: data/model/ hash: md5 - md5: bdaaf823857f9dc7b6ee2d4b88927cc1.dir - size: 805896324 - nfiles: 31 + md5: 18bd7a93ece75a65d3a950b7dfdab4fb.dir + size: 735951861 + nfiles: 35 - path: metrics/fit_metrics.json hash: md5 - md5: 0ed5b1141bbb8bc3156e7c056b29f3cd - size: 225 + md5: 8a952a5e884c268e6059357a627b9251 + size: 224 generate_predictions: cmd: python 3_generate_predictions.py deps: @@ -96,13 +155,13 @@ stages: size: 2464 - path: data/model hash: md5 - md5: bdaaf823857f9dc7b6ee2d4b88927cc1.dir - size: 805896324 - nfiles: 31 + md5: 18bd7a93ece75a65d3a950b7dfdab4fb.dir + size: 735951861 + nfiles: 35 - path: data/prepared_data hash: md5 - md5: 3c77fa10cd1cd503eb4d2540394629f6.dir - size: 42626894 + md5: efa416abea618ae6220a0c3d597603cf.dir + size: 44750997 nfiles: 2 params: configs/settings.yaml: @@ -114,8 +173,8 @@ stages: outs: - path: data/predictions/ hash: md5 - md5: 38707d16ae1e2330cc03f524db9cdd60.dir - size: 648730 + md5: 07ef721a0dc94a52e3ba7a70ac45b8ff.dir + size: 463563 nfiles: 1 generate_metrics: cmd: python 4_generate_metrics.py @@ -126,13 +185,13 @@ stages: size: 3484 - path: data/predictions hash: md5 - md5: 38707d16ae1e2330cc03f524db9cdd60.dir - size: 648730 + md5: 07ef721a0dc94a52e3ba7a70ac45b8ff.dir + size: 463563 nfiles: 1 - path: data/prepared_data hash: md5 - md5: 3c77fa10cd1cd503eb4d2540394629f6.dir - size: 42626894 + md5: efa416abea618ae6220a0c3d597603cf.dir + size: 44750997 nfiles: 2 params: configs/settings.yaml: @@ -142,16 +201,30 @@ stages: outs: - path: metrics/metrics.json hash: md5 - md5: 145e7ac84ab4a4407b23695a632b4d91 - size: 226 - startup_cleanup: - cmd: python 0_startup_cleanup.py + md5: 9f863f47799d42c101eba3b03a179455 + size: 224 + generate_scenerio_metrics: + cmd: python 5_generate_scenarios.py deps: - - path: 0_startup_cleanup.py + - path: 5_generate_scenarios.py hash: md5 - md5: b1b12f6b6393fbf8b83d23684df0a3d4 - size: 1220 + md5: a18f6c6ae2082f038df47386cf3e418e + size: 4896 params: - configs/settings.yaml: - default.startup_cleanup.artefacts: ./data - default.startup_cleanup.metrics: ./metrics + configs/scenarios.yaml: + default.scenarios: + input_dataclient_type: aws-s3 + output_dataclient_type: local + scenario_data_filepaths: + - s3://retrofit-data-dev/scenario_data/27-03-2024-11-38-15/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: 64e7db945ff655ae03c20c9845f19106 + size: 363 + - path: metrics/scenario_table.md + hash: md5 + md5: d4f8afe07b774374aeaa48f1b7b8a5fc + 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 0d259fb..258981d 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==1.5.3 -autogluon==0.8.2 +pandas==2.1.4 +autogluon==1.0.0 dynaconf==3.2.0 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 afad9be..2ab48e9 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==1.5.3 -autogluon==0.8.2 +pandas==2.1.4 +autogluon==1.0.0 dynaconf==3.2.0 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 d8c5907..2024d84 100644 --- a/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt +++ b/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt @@ -1,9 +1,10 @@ joblib==1.3.2 boto3==1.28.17 -pandas==1.5.3 -autogluon==0.8.2 +pandas==2.1.4 +autogluon==1.0.0 +ray==2.6.3 dynaconf==3.2.0 -alibi==0.9.4 +alibi==0.9.5 shap==0.42.1 pyarrow==13.0.0 pre-commit==3.3.3 diff --git a/modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt b/modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt index bbdc2fa..84452a3 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==1.5.3 -autogluon==0.8.2 +pandas==2.1.4 +autogluon==1.0.0 dynaconf==3.2.0