199 lines
18 KiB
Python
199 lines
18 KiB
Python
configfile: "config.yaml"
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include: "rules/renv.snakefile"
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include: "rules/preprocessing.snakefile"
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include: "rules/features.snakefile"
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include: "rules/models.snakefile"
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include: "rules/reports.snakefile"
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include: "rules/mystudy.snakefile" # You can add snakfiles with rules tailored to your project
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import itertools
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files_to_compute = []
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if len(config["PIDS"]) == 0:
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raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external")
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if config["MESSAGES"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{day_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], day_segment = config["MESSAGES"]["DAY_SEGMENTS"]))
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if config["CALLS"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], segment = config["CALLS"]["DAY_SEGMENTS"]))
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if config["BARNETT_LOCATION"]["COMPUTE"]:
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# TODO add files_to_compute.extend(optional_location_input(None))
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if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED" and config["BARNETT_LOCATION"]["DB_TABLE"] not in config["TABLES_FOR_SENSED_BINS"]:
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raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to TABLES_FOR_SENSED_BINS in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)")
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/location_barnett_{segment}.csv", pid=config["PIDS"], segment = config["BARNETT_LOCATION"]["DAY_SEGMENTS"]))
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if config["BLUETOOTH"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/bluetooth_{segment}.csv", pid=config["PIDS"], segment = config["BLUETOOTH"]["DAY_SEGMENTS"]))
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if config["ACTIVITY_RECOGNITION"]["COMPUTE"]:
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# TODO add files_to_compute.extend(optional_ar_input(None)), the Android or iOS table gets processed depending on each participant
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files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{segment}.csv",pid=config["PIDS"], segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"]))
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if config["BATTERY"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"]))
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if config["SCREEN"]["COMPUTE"]:
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if config["SCREEN"]["DB_TABLE"] not in config["TABLES_FOR_SENSED_BINS"]:
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raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to TABLES_FOR_SENSED_BINS in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)")
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"]))
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if config["LIGHT"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"]))
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if config["ACCELEROMETER"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{day_segment}.csv", pid = config["PIDS"], day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"]))
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if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"]))
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if config["WIFI"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"]))
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if config["HEARTRATE"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"]))
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files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"]))
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if config["STEP"]["COMPUTE"]:
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if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED":
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"]))
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files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"]))
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if config["SLEEP"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"]))
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files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"]))
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if config["CONVERSATION"]["COMPUTE"]:
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# TODO add files_to_compute.extend(optional_conversation_input(None)), the Android or iOS table gets processed depending on each participant
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files_to_compute.extend(expand("data/processed/{pid}/conversation_{segment}.csv",pid=config["PIDS"], segment = config["CONVERSATION"]["DAY_SEGMENTS"]))
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if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]:
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rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"]
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cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]
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models, scalers, rows_nan_thresholds, cols_nan_thresholds = [], [], [], []
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for model_name in config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"]:
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models = models + [model_name] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) * len(rows_nan_threshold)
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scalers = scalers + config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name] * len(rows_nan_threshold)
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rows_nan_thresholds = rows_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in rows_nan_threshold))
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cols_nan_thresholds = cols_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in cols_nan_threshold))
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results = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"] + ["merged_population_model_results"]
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files_to_compute.extend(expand("data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_original.csv",
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pid = config["PIDS"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]))
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files_to_compute.extend(expand("data/processed/data_for_population_model/{source}_{day_segment}_original.csv",
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]))
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files_to_compute.extend(expand(
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expand("data/processed/{pid}/data_for_individual_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv",
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pid = config["PIDS"],
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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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cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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zip,
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rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
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cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
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files_to_compute.extend(expand(
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expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv",
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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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cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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zip,
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rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
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cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
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files_to_compute.extend(expand("data/processed/data_for_population_model/demographic_features.csv"))
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files_to_compute.extend(expand("data/processed/data_for_population_model/targets_{summarised}.csv",
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summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]))
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files_to_compute.extend(expand(
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expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv",
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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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cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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zip,
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rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
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cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
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files_to_compute.extend(expand(
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expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv",
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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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cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
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summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]),
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zip,
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rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
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cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
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files_to_compute.extend(expand(
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expand("data/processed/output_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.csv",
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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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cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
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cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
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summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]),
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zip,
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rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
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cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
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files_to_compute.extend(expand(
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expand("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}}/{result}.csv",
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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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cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
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cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
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summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"],
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result = results),
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zip,
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rows_nan_threshold = rows_nan_thresholds,
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cols_nan_threshold = cols_nan_thresholds,
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model = models,
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scaler = scalers))
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rule all:
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input:
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files_to_compute
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rule clean:
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shell:
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"rm -rf data/raw/* && rm -rf data/interim/* && rm -rf data/processed/* && rm -rf reports/figures/* && rm -rf reports/*.zip && rm -rf reports/compliance/*" |