diff --git a/.github/workflows/MLPipelinePullRequest.yml b/.github/workflows/MLPipelinePullRequest.yml index cbc379d..493aef9 100644 --- a/.github/workflows/MLPipelinePullRequest.yml +++ b/.github/workflows/MLPipelinePullRequest.yml @@ -98,6 +98,10 @@ jobs: git fetch --depth=1 origin ${TARGET_BRANCH}:${TARGET_BRANCH} dvc metrics diff --md --all ${TARGET_BRANCH} >> report.md + echo "## Scenario metrics" >> report.md + + cat metrics/scenario_table.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..28bcb9d --- /dev/null +++ b/modules/ml-pipeline/src/pipeline/5_generate_scenarios.py @@ -0,0 +1,125 @@ +""" +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 configs.post_prediction_logic import post_prediction_logic +from core.DataClient import dataclient_factory +from core.MLModels import model_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"] +output_filepath = scenarios_params["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], +) + + +def generate_scenario_predictions( + input_dataclient: DataClient, + output_dataclient: DataClient, + model: MLModel, + model_filepath: str, + scenario_data_filepaths: list, + predictions_column_name: str, + 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("--- Save prediction into metrics ---") + + output_df = scenario_data[["uprn", "id", "impact", "predicted_impact"]] + + output_dataclient.save_data( + obj=output_df, location=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, + model_filepath=model_filepath, + scenario_data_filepaths=scenario_data_filepaths, + predictions_column_name=predictions_column_name, + output_filepath=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 6fbf094..a36bfbc 100644 --- a/modules/ml-pipeline/src/pipeline/configs/build_model.yaml +++ b/modules/ml-pipeline/src/pipeline/configs/build_model.yaml @@ -15,8 +15,8 @@ default: problem_type: regression eval_metric: mean_squared_error #mean_absolute_error time_limit: 1800 - presets: good_quality - excluded_model_types: ['RF', 'FASTAI', 'CAT', 'NN_TORCH', 'KNN', 'XT'] + 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} 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..e76336a --- /dev/null +++ b/modules/ml-pipeline/src/pipeline/configs/scenarios.yaml @@ -0,0 +1,8 @@ +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 + output_filepath: ./metrics/scenario_table.md diff --git a/modules/ml-pipeline/src/pipeline/configs/settings.yaml b/modules/ml-pipeline/src/pipeline/configs/settings.yaml index 4757d91..f42b2be 100644 --- a/modules/ml-pipeline/src/pipeline/configs/settings.yaml +++ b/modules/ml-pipeline/src/pipeline/configs/settings.yaml @@ -18,12 +18,7 @@ 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 + 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/dvc.lock b/modules/ml-pipeline/src/pipeline/dvc.lock index 530a3c8..fcc035b 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: @@ -17,12 +28,19 @@ 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: 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 @@ -31,8 +49,8 @@ stages: outs: - path: data/prepared_data/ hash: md5 - md5: 3d1144848fce4ce50f6abfaec5235552.dir - size: 46392840 + md5: efa416abea618ae6220a0c3d597603cf.dir + size: 44750997 nfiles: 2 build_model: cmd: python 2_build_model.py @@ -43,8 +61,8 @@ stages: size: 4820 - path: data/prepared_data hash: md5 - md5: 3d1144848fce4ce50f6abfaec5235552.dir - size: 46392840 + md5: efa416abea618ae6220a0c3d597603cf.dir + size: 44750997 nfiles: 2 params: configs/build_model.yaml: @@ -62,10 +80,9 @@ stages: problem_type: regression eval_metric: mean_squared_error time_limit: 1800 - presets: good_quality + presets: medium_quality excluded_model_types: - RF - - FASTAI - CAT - NN_TORCH - KNN @@ -77,18 +94,18 @@ stages: outs: - path: data/fit_predictions/ hash: md5 - md5: 346b6611afbf2070e038bf945249a86e.dir - size: 3384302 + md5: de46250d454c4d713ab580b10ff3fd31.dir + size: 3349318 nfiles: 1 - path: data/model/ hash: md5 - md5: 8e37f21728cd092660bafa8c32dc109f.dir - size: 423840922 - nfiles: 118 + md5: 18bd7a93ece75a65d3a950b7dfdab4fb.dir + size: 735951861 + nfiles: 35 - path: metrics/fit_metrics.json hash: md5 - md5: d63e1a8d31503055835ac35149554e41 - size: 223 + md5: 8a952a5e884c268e6059357a627b9251 + size: 224 generate_predictions: cmd: python 3_generate_predictions.py deps: @@ -98,13 +115,13 @@ stages: size: 2464 - path: data/model hash: md5 - md5: 8e37f21728cd092660bafa8c32dc109f.dir - size: 423840922 - nfiles: 118 + md5: 18bd7a93ece75a65d3a950b7dfdab4fb.dir + size: 735951861 + nfiles: 35 - path: data/prepared_data hash: md5 - md5: 3d1144848fce4ce50f6abfaec5235552.dir - size: 46392840 + md5: efa416abea618ae6220a0c3d597603cf.dir + size: 44750997 nfiles: 2 params: configs/settings.yaml: @@ -116,8 +133,8 @@ stages: outs: - path: data/predictions/ hash: md5 - md5: d148baf508140353d62c16d6ab0fb6b7.dir - size: 469224 + md5: 07ef721a0dc94a52e3ba7a70ac45b8ff.dir + size: 463563 nfiles: 1 generate_metrics: cmd: python 4_generate_metrics.py @@ -128,13 +145,13 @@ stages: size: 3484 - path: data/predictions hash: md5 - md5: d148baf508140353d62c16d6ab0fb6b7.dir - size: 469224 + md5: 07ef721a0dc94a52e3ba7a70ac45b8ff.dir + size: 463563 nfiles: 1 - path: data/prepared_data hash: md5 - md5: 3d1144848fce4ce50f6abfaec5235552.dir - size: 46392840 + md5: efa416abea618ae6220a0c3d597603cf.dir + size: 44750997 nfiles: 2 params: configs/settings.yaml: @@ -144,16 +161,25 @@ stages: outs: - path: metrics/metrics.json hash: md5 - md5: 196232f94b563ac525cf65ee5cc6d639 - size: 222 - 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: 30f80ffeb6ee50c5f7b82943a4dc7702 + size: 4014 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/24-03-2024-20-23-25/recommendations_scoring_data.parquet + output_filepath: ./metrics/scenario_table.md + outs: + - path: metrics/scenario_table.md + hash: md5 + md5: 54856c66fca8b2ebd1fa4dea2d25734a + size: 2133 diff --git a/modules/ml-pipeline/src/pipeline/dvc.yaml b/modules/ml-pipeline/src/pipeline/dvc.yaml index 58889cc..5ce35ce 100644 --- a/modules/ml-pipeline/src/pipeline/dvc.yaml +++ b/modules/ml-pipeline/src/pipeline/dvc.yaml @@ -71,6 +71,16 @@ 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 + 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..189c2ee 100644 --- a/modules/ml-pipeline/src/pipeline/metrics/.gitignore +++ b/modules/ml-pipeline/src/pipeline/metrics/.gitignore @@ -1,2 +1,3 @@ /fit_metrics.json /metrics.json +/scenario_table.md