def input_merge_features_of_single_participant(wildcards): if wildcards.source == "phone_fitbit_features": return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"]["PHONE_FEATURES"] + config["PARAMS_FOR_ANALYSIS"]["FITBIT_FEATURES"], day_segment=wildcards.day_segment) else: return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"][wildcards.source.upper()], day_segment=wildcards.day_segment) def optional_input_days_to_include(wildcards): if config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["ENABLED"]: # This input automatically trigers the rule days_to_analyse in mystudy.snakefile return ["data/interim/{pid}/days_to_analyse" + \ "_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_BEFORE_SURGERY"]) + \ "_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_IN_HOSPITAL"]) + \ "_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_AFTER_DISCHARGE"]) + ".csv"] else: return [] def optional_input_valid_sensed_days(wildcards): if config["PARAMS_FOR_ANALYSIS"]["DROP_VALID_SENSED_DAYS"]["ENABLED"]: # This input automatically trigers the rule phone_valid_sensed_days in preprocessing.snakefile return ["data/interim/{pid}/phone_valid_sensed_days.csv"] else: return [] rule merge_features_for_individual_model: input: feature_files = input_merge_features_of_single_participant, phone_valid_sensed_days = optional_input_valid_sensed_days, days_to_include = optional_input_days_to_include params: source = "{source}" output: "data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_original.csv" script: "../src/models/merge_features_for_individual_model.R" rule merge_features_for_population_model: input: feature_files = expand("data/processed/{pid}/data_for_individual_model/{{source}}_{{day_segment}}_original.csv", pid=config["PIDS"]) output: "data/processed/data_for_population_model/{source}_{day_segment}_original.csv" script: "../src/models/merge_features_for_population_model.R" rule merge_demographicfeatures_for_population_model: input: data_files = expand("data/processed/{pid}/demographic_features.csv", pid=config["PIDS"]) output: "data/processed/data_for_population_model/demographic_features.csv" script: "../src/models/merge_data_for_population_model.py" rule merge_targets_for_population_model: input: data_files = expand("data/processed/{pid}/targets_{{summarised}}.csv", pid=config["PIDS"]) output: "data/processed/data_for_population_model/targets_{summarised}.csv" script: "../src/models/merge_data_for_population_model.py" rule clean_features_for_individual_model: input: rules.merge_features_for_individual_model.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"], days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"] output: "data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_clean.csv" script: "../src/models/clean_features_for_model.R" rule clean_features_for_population_model: input: rules.merge_features_for_population_model.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"], days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"] output: "data/processed/data_for_population_model/{source}_{day_segment}_clean.csv" script: "../src/models/clean_features_for_model.R" rule modeling: input: cleaned_features = "data/processed/data_for_population_model/{source}_{day_segment}_clean.csv", demographic_features = "data/processed/data_for_population_model/demographic_features.csv", targets = "data/processed/data_for_population_model/targets_{summarised}.csv", params: model = "{model}", cv_method = "{cv_method}", source = "{source}", day_segment = "{day_segment}", summarised = "{summarised}", scaler = "{scaler}", cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], numerical_operators = config["PARAMS_FOR_ANALYSIS"]["NUMERICAL_OPERATORS"], categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"], categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_DEMOGRAPHIC_FEATURES"], model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"], rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}" output: fold_predictions = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_predictions.csv", fold_metrics = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_metrics.csv", overall_results = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/overall_results.csv", fold_feature_importances = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_feature_importances.csv" script: "../src/models/modeling.py"