Simplify window workflow
JulioV
parent
a0b5b5982b
commit
026d056378
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@ -107,5 +107,6 @@ reports/
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.RData
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.Rhistory
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sn_profile_*/
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!sn_profile_rapids
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settings.dcf
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tests/fakedata_generation/
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17
config.yaml
17
config.yaml
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@ -4,20 +4,7 @@ PIDS: [t01]
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# Global var with common day segments
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DAY_SEGMENTS: &day_segments
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[daily, morning, afternoon, evening, night]
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DAY_SEGMENTS2: &day_segments2
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# Day segments can be computed based on three strategies
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# Frequency based: Set SEGMENTS to a number representing the length of a segment in minutes: 15. Every day will be divided in n segments of SEGMENTS minutes starting at midnight.
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# Interval based: Set SEGMENTS to a string containing a JSON array with an element for each segment containing a label, and start and end time in 24 hour format.
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# For example: '{"daily": {"00:00", "23:59"}, "morning": {"06:00", "11:59"}}'. Note the string is single quoted and each value double quoted.
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# Event based: Set SEGMENTS to a string with a path to a csv file with two columns, a unix timestamp column in milliseconds called "timestamp" and a string column called "label".
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# Every row represents a meaningful event around which features will be extracted, each label should be unique. See EVENT_TIME_SHIFT and EVENT_SEGMENT_DURATION
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# If you want daily features, create a segment with label "daily". DO NOT use "daily" to label any other segment
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# ------------------------------------------------------------------------------
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SEGMENTS: '[["daily", "00:00", "23:59"], ["morning", "06:00", "11:59"], ["evening", "18:00", "23:59"]]'
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EVENT_TIME_SHIFT: 0 # Postive or negative number of minutes. A day segment will start EVENT_TIME_SHIFT minutes before or after each meaningful event. Only used if SEGMENTS is a valid event file (see above).
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EVENT_SEGMENT_DURATION: 60 # Lengh of every day_segment around each meaningful event. Only used if SEGMENTS is a valid event file (see above).
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"data/external/daysegments_default.csv"
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# Global timezone
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# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
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@ -110,7 +97,7 @@ DORYAB_LOCATION:
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BLUETOOTH:
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COMPUTE: False
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DB_TABLE: bluetooth
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DAY_SEGMENTS: *day_segments2
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DAY_SEGMENTS: *day_segments
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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ACTIVITY_RECOGNITION:
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@ -111,29 +111,11 @@ def optional_heatmap_days_by_sensors_input(wildcards):
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tables_platform = [table for table in config["HEATMAP_DAYS_BY_SENSORS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist
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return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform)
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def optional_day_segments_input(wildcards):
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return []
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def find_day_segments_argument(wildcards, argument):
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def find_day_segments_input_file(wildcards):
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for key, values in config.items():
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if "DAY_SEGMENTS" in config[key] and "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor:
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return config[key]["DAY_SEGMENTS"][argument]
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def hash_day_segments(config_section):
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# TODO hash the content of the interval file instead of SEGMENTS when SEGMENTS is a path
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return hashlib.sha1(config_section["SEGMENTS"].encode('utf-8')).hexdigest()
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def is_valid_day_segment_configuration(sensor, config_section):
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if not (isinstance(config_section, collections.OrderedDict) or isinstance(config_section, dict)):
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raise ValueError("The DAY_SEGMENTS parameter in the {} config section should be a dictionary with three parameters: SEGMENTS (str), EVENT_TIME_SHIFT (int), and EVENT_SEGMENT_DURATION (int)".format(sensor))
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for attribute in ["SEGMENTS", "EVENT_TIME_SHIFT", "EVENT_SEGMENT_DURATION"]:
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if not attribute in config_section:
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raise ValueError("The config[{}][DAY_SEGMENTS] section should have an attribute named {}".format(sensor, attribute))
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if not isinstance(config_section["SEGMENTS"], str):
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raise ValueError("The config[{}][DAY_SEGMENTS][SEGMENTS] variable should be a string".format(sensor))
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if not isinstance(config_section["EVENT_TIME_SHIFT"], int):
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raise ValueError("The config[{}][DAY_SEGMENTS][EVENT_TIME_SHIFT] variable should be an integer".format(sensor))
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if not isinstance(config_section["EVENT_SEGMENT_DURATION"], int):
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raise ValueError("The config[{}][DAY_SEGMENTS][EVENT_SEGMENT_DURATION] variable should be an integer".format(sensor))
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return True
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if "DB_TABLE" in config[key] and config[key]["DB_TABLE"] == wildcards.sensor:
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if "DAY_SEGMENTS" in config[key]:
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return config[key]["DAY_SEGMENTS"]
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else:
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raise ValueError("{} should have a DAY_SEGMENTS parameter containing the path to its day segments file".format(wildcards.sensor))
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@ -88,12 +88,12 @@ rule location_doryab_features:
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rule bluetooth_features:
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input:
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expand("data/raw/{{pid}}/{sensor}_with_datetime_{{hash}}.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]),
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day_segments = expand("data/interim/{{pid}}/{sensor}_day_segments_{{hash}}.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])
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expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]),
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day_segments = expand("data/interim/{sensor}_day_segments.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])
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params:
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features = config["BLUETOOTH"]["FEATURES"]
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output:
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"data/processed/{pid}/bluetooth_{hash}.csv"
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"data/processed/{pid}/bluetooth_features.csv"
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script:
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"../src/features/bluetooth_features.R"
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@ -192,12 +192,12 @@ rule applications_foreground_features:
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rule wifi_features:
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input:
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unpack(optional_wifi_input)
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expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]),
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day_segments = expand("data/interim/{sensor}_day_segments.csv", sensor=config["WIFI"]["DB_TABLE"])
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params:
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day_segment = "{day_segment}",
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features = config["WIFI"]["FEATURES"]
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output:
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"data/processed/{pid}/wifi_{day_segment}.csv"
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"data/processed/{pid}/wifi_features.csv"
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script:
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"../src/features/wifi_features.R"
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@ -40,13 +40,9 @@ rule download_dataset:
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rule compute_day_segments:
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input:
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optional_day_segments_input,
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params:
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segments = lambda wildcards: find_day_segments_argument(wildcards, "SEGMENTS"),
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event_time_shift = lambda wildcards: find_day_segments_argument(wildcards, "EVENT_TIME_SHIFT"),
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event_segment_duration = lambda wildcards: find_day_segments_argument(wildcards, "EVENT_SEGMENT_DURATION"),
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find_day_segments_input_file
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output:
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"data/interim/{pid}/{sensor}_day_segments_{hash}.csv"
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segments_file = "data/interim/{sensor}_day_segments.csv",
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script:
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"../src/data/compute_day_segments.py"
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@ -63,14 +59,14 @@ if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0:
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rule readable_datetime:
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input:
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sensor_input = "data/raw/{pid}/{sensor}_raw.csv",
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day_segments = "data/interim/{pid}/{sensor}_day_segments_{hash}.csv"
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day_segments = "data/interim/{sensor}_day_segments.csv"
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params:
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timezones = None,
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fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"]
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wildcard_constraints:
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sensor = '.*(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ').*' # only process smartphone sensors, not fitbit
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output:
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"data/raw/{pid}/{sensor}_with_datetime_{hash}.csv"
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"data/raw/{pid}/{sensor}_with_datetime.csv"
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script:
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"../src/data/readable_datetime.R"
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@ -0,0 +1,231 @@
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import itertools
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import hashlib
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import collections
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configfile: "config.yaml"
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include: "../rules/common.smk"
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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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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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files_to_compute = []
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if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"]:
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if len(config["PHONE_VALID_SENSED_BINS"]["TABLES"]) == 0:
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raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml")
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files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]))
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if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]:
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if len(config["PHONE_VALID_SENSED_BINS"]["TABLES"]) == 0:
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raise ValueError("If you want to compute PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml")
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files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days.csv", pid=config["PIDS"]))
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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}_{day_segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], day_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":
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if config["BARNETT_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]:
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files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]))
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else:
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raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] 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_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BARNETT_LOCATION"]["DAY_SEGMENTS"]))
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if config["BLUETOOTH"]["COMPUTE"]:
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files_to_compute.extend(expand("data/interim/{sensor}_day_segments.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]))
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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_features.csv", pid=config["PIDS"] ))
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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_{day_segment}.csv",pid=config["PIDS"], day_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"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]:
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files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]))
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else:
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raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] 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/interim/{sensor}_day_segments.csv", sensor=config["WIFI"]["DB_TABLE"]))
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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_features.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_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"]))
|
||||
|
||||
if config["DORYAB_LOCATION"]["COMPUTE"]:
|
||||
if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED":
|
||||
if config["DORYAB_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]:
|
||||
files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]))
|
||||
else:
|
||||
raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] 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)")
|
||||
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"]))
|
||||
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"]))
|
||||
files_to_compute.extend(expand("data/processed/{pid}/location_doryab_{segment}.csv", pid=config["PIDS"], segment = config["DORYAB_LOCATION"]["DAY_SEGMENTS"]))
|
||||
|
||||
if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]:
|
||||
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"]
|
||||
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]
|
||||
models, scalers, rows_nan_thresholds, cols_nan_thresholds = [], [], [], []
|
||||
for model_name in config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"]:
|
||||
models = models + [model_name] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) * len(rows_nan_threshold)
|
||||
scalers = scalers + config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name] * len(rows_nan_threshold)
|
||||
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))
|
||||
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))
|
||||
results = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"] + ["merged_population_model_results"]
|
||||
|
||||
files_to_compute.extend(expand("data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_original.csv",
|
||||
pid = config["PIDS"],
|
||||
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
|
||||
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]))
|
||||
files_to_compute.extend(expand("data/processed/data_for_population_model/{source}_{day_segment}_original.csv",
|
||||
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
|
||||
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]))
|
||||
files_to_compute.extend(expand(
|
||||
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",
|
||||
pid = config["PIDS"],
|
||||
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
|
||||
days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
|
||||
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
|
||||
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
|
||||
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
|
||||
zip,
|
||||
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
|
||||
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
|
||||
files_to_compute.extend(expand(
|
||||
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",
|
||||
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
|
||||
days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
|
||||
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
|
||||
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
|
||||
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
|
||||
zip,
|
||||
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
|
||||
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
|
||||
files_to_compute.extend(expand("data/processed/data_for_population_model/demographic_features.csv"))
|
||||
files_to_compute.extend(expand("data/processed/data_for_population_model/targets_{summarised}.csv",
|
||||
summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]))
|
||||
files_to_compute.extend(expand(
|
||||
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",
|
||||
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
|
||||
days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
|
||||
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
|
||||
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
|
||||
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
|
||||
zip,
|
||||
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
|
||||
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
|
||||
files_to_compute.extend(expand(
|
||||
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",
|
||||
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
|
||||
days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
|
||||
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
|
||||
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
|
||||
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
|
||||
summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]),
|
||||
zip,
|
||||
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
|
||||
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
|
||||
files_to_compute.extend(expand(
|
||||
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",
|
||||
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
|
||||
days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
|
||||
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
|
||||
cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"],
|
||||
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
|
||||
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
|
||||
summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]),
|
||||
zip,
|
||||
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
|
||||
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
|
||||
files_to_compute.extend(expand(
|
||||
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",
|
||||
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
|
||||
days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
|
||||
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
|
||||
cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"],
|
||||
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
|
||||
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
|
||||
summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"],
|
||||
result = results),
|
||||
zip,
|
||||
rows_nan_threshold = rows_nan_thresholds,
|
||||
cols_nan_threshold = cols_nan_thresholds,
|
||||
model = models,
|
||||
scaler = scalers))
|
||||
rule all:
|
||||
input:
|
||||
files_to_compute
|
||||
|
||||
rule clean:
|
||||
shell:
|
||||
"rm -rf data/raw/* && rm -rf data/interim/* && rm -rf data/processed/* && rm -rf reports/figures/* && rm -rf reports/*.zip && rm -rf reports/compliance/*"
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
configfile: ./sn_profile_rapids/pipeline_config.yaml
|
||||
directory: ./
|
||||
snakefile: ./sn_profile_rapids/Snakefile
|
||||
cores: 1
|
||||
# forcerun: compute_day_segments
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
PIDS: [t01]
|
||||
DOWNLOAD_DATASET:
|
||||
GROUP: RAPIDS
|
||||
BLUETOOTH:
|
||||
COMPUTE: True
|
||||
DAY_SEGMENTS: "data/external/daysegments_bluetooth.csv"
|
||||
WIFI:
|
||||
COMPUTE: True
|
||||
|
|
@ -1,25 +1,19 @@
|
|||
import pandas as pd
|
||||
import json
|
||||
|
||||
def parse_day_segments(segments, event_time_shift, event_segment_duration):
|
||||
# Temporal code to parse segments, should substitute with the code to parse
|
||||
# frequencies, intervals, and events
|
||||
data = json.loads(segments)
|
||||
label = []
|
||||
start = []
|
||||
end = []
|
||||
for d in data:
|
||||
label.append(d[0])
|
||||
start.append(d[1])
|
||||
end.append(d[2])
|
||||
|
||||
day_segments = pd.DataFrame(list(zip([1]*len(label), start, end, label)), columns =['local_date','start_time','end_time','label'])
|
||||
def parse_day_segments(day_segments):
|
||||
# Add code to parse frequencies, intervals, and events
|
||||
# Expected formats:
|
||||
# Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int)
|
||||
# Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format
|
||||
# Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms, length is in minutes (int), shift is in minutes (+/-int) and is add/substracted from timestamp
|
||||
# Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date
|
||||
|
||||
day_segments["local_date"] = 1
|
||||
day_segments = day_segments.rename(columns={"start": "start_time", "end":"end_time"})
|
||||
return day_segments
|
||||
##########################
|
||||
|
||||
segments = snakemake.params["segments"]
|
||||
event_time_shift = snakemake.params["event_time_shift"]
|
||||
event_segment_duration = snakemake.params["event_segment_duration"]
|
||||
|
||||
day_segments = parse_day_segments(segments, event_time_shift, event_segment_duration)
|
||||
day_segments.to_csv(snakemake.output[0], index=False)
|
||||
day_segments = pd.read_csv(snakemake.input[0])
|
||||
day_segments = parse_day_segments(day_segments)
|
||||
day_segments.to_csv(snakemake.output["segments_file"], index=False)
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
library(dplyr)
|
||||
|
||||
filter_by_day_segment <- function(data, day_segment) {
|
||||
if(day_segment %in% c("morning", "afternoon", "evening", "night"))
|
||||
if(day_segment != "daily")
|
||||
data <- data %>% filter(local_day_segment == day_segment)
|
||||
|
||||
return(data %>% group_by(local_date))
|
||||
|
|
|
|||
|
|
@ -16,14 +16,18 @@ if(!is.null(snakemake@input[["visible_access_points"]]) && is.null(snakemake@inp
|
|||
wifi_data <- bind_rows(visible_access_points, connected_access_points) %>% arrange(timestamp)
|
||||
}
|
||||
|
||||
day_segment <- snakemake@params[["day_segment"]]
|
||||
wifi_data <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE)
|
||||
day_segments <- read.csv(snakemake@input[["day_segments"]])
|
||||
requested_features <- snakemake@params[["features"]]
|
||||
features = data.frame(local_date = character(), stringsAsFactors = FALSE)
|
||||
|
||||
# Compute base wifi features
|
||||
features <- merge(features, base_wifi_features(wifi_data, day_segment, requested_features), by="local_date", all = TRUE)
|
||||
|
||||
if(ncol(features) != length(requested_features) + 1)
|
||||
day_segments <- day_segments %>% distinct(label) %>% pull(label)
|
||||
# Compute base wifi features
|
||||
for (day_segment in day_segments)
|
||||
features <- merge(features, base_wifi_features(wifi_data, day_segment, requested_features), by="local_date", all = TRUE)
|
||||
|
||||
if(ncol(features) != (length(requested_features)) * length(day_segments) + 1)
|
||||
stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your wifi feature extraction functions"))
|
||||
|
||||
write.csv(features, snakemake@output[[1]], row.names = FALSE)
|
||||
|
|
|
|||
Loading…
Reference in New Issue