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