rule download_demographic_data: input: participant_file = "data/external/participant_files/{pid}.yaml" params: source = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["SOURCE"], table = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["TABLE"], output: "data/raw/{pid}/participant_info_raw.csv" script: "../src/data/workflow_example/download_demographic_data.R" rule demographic_features: input: participant_info = "data/raw/{pid}/participant_info_raw.csv" params: pid = "{pid}", features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FEATURES"] output: "data/processed/features/{pid}/demographic_features.csv" script: "../src/features/workflow_example/demographic_features.py" rule download_target_data: input: participant_file = "data/external/participant_files/{pid}.yaml" params: source = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["SOURCE"], table = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["TABLE"], output: "data/raw/{pid}/participant_target_raw.csv" script: "../src/data/workflow_example/download_target_data.R" rule target_readable_datetime: input: sensor_input = "data/raw/{pid}/participant_target_raw.csv", time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: fixed_timezone = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["SOURCE"]["TIMEZONE"], time_segments_type = config["TIME_SEGMENTS"]["TYPE"], include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: "data/raw/{pid}/participant_target_with_datetime.csv" script: "../src/data/readable_datetime.R" rule parse_targets: input: targets = "data/raw/{pid}/participant_target_with_datetime.csv", time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" output: "data/processed/targets/{pid}/parsed_targets.csv" script: "../src/models/workflow_example/parse_targets.py" rule clean_sensor_features_for_individual_participants: input: rules.merge_sensor_features_for_individual_participants.output params: cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"], cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], data_yielded_hours_ratio_threshold = config["PARAMS_FOR_ANALYSIS"]["DATA_YIELDED_HOURS_RATIO_THRESHOLD"], output: "data/processed/features/{pid}/all_sensor_features_cleaned.csv" script: "../src/models/workflow_example/clean_sensor_features.R" rule clean_sensor_features_for_all_participants: input: rules.merge_sensor_features_for_all_participants.output params: cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"], cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], data_yielded_hours_ratio_threshold = config["PARAMS_FOR_ANALYSIS"]["DATA_YIELDED_HOURS_RATIO_THRESHOLD"], output: "data/processed/features/all_participants/all_sensor_features_cleaned.csv" script: "../src/models/workflow_example/clean_sensor_features.R" rule merge_features_and_targets_for_individual_model: input: cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned.csv", targets = "data/processed/targets/{pid}/parsed_targets.csv", output: "data/processed/models/individual_model/{pid}/input.csv" script: "../src/models/workflow_example/merge_features_and_targets_for_individual_model.py" rule merge_features_and_targets_for_population_model: input: cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned.csv", demographic_features = expand("data/processed/features/{pid}/demographic_features.csv", pid=config["PIDS"]), targets = expand("data/processed/targets/{pid}/parsed_targets.csv", pid=config["PIDS"]), output: "data/processed/models/population_model/input.csv" script: "../src/models/workflow_example/merge_features_and_targets_for_population_model.py" rule baselines_for_individual_model: input: "data/processed/models/individual_model/{pid}/input.csv" params: cv_method = "{cv_method}", colnames_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FEATURES"], output: "data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv" log: "data/processed/models/individual_model/{pid}/output_{cv_method}/baselines_notes.log" script: "../src/models/workflow_example/baselines.py" rule baselines_for_population_model: input: "data/processed/models/population_model/input.csv" params: cv_method = "{cv_method}", colnames_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FEATURES"], output: "data/processed/models/population_model/output_{cv_method}/baselines.csv" log: "data/processed/models/population_model/output_{cv_method}/baselines_notes.log" script: "../src/models/workflow_example/baselines.py" rule modelling_for_individual_participants: input: data = "data/processed/models/individual_model/{pid}/input.csv" params: model = "{model}", cv_method = "{cv_method}", scaler = "{scaler}", categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"], categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["CATEGORICAL_FEATURES"], model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"], output: fold_predictions = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/fold_predictions.csv", fold_metrics = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/fold_metrics.csv", overall_results = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/overall_results.csv", fold_feature_importances = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/fold_feature_importances.csv" log: "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/notes.log" script: "../src/models/workflow_example/modelling.py" rule modelling_for_all_participants: input: data = "data/processed/models/population_model/input.csv" params: model = "{model}", cv_method = "{cv_method}", scaler = "{scaler}", categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"], categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["CATEGORICAL_FEATURES"], model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"], output: fold_predictions = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/fold_predictions.csv", fold_metrics = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/fold_metrics.csv", overall_results = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/overall_results.csv", fold_feature_importances = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/fold_feature_importances.csv" log: "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/notes.log" script: "../src/models/workflow_example/modelling.py"