Split FITBIT_STEPS into FITBIT_STEPS_SUMMARY and FITBIT_STEPS_INTRADAY
Meng Li
parent
b7e22b7440
commit
a71efd6b85
25
Snakefile
25
Snakefile
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@ -144,9 +144,6 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys():
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files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"]))
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if config["FITBIT_STEPS"]["TABLE_FORMAT"] not in ["JSON", "CSV"]:
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raise ValueError("config['FITBIT_STEPS']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_STEPS"]["TABLE_FORMAT"])
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if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]:
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raise ValueError("config['FITBIT_CALORIES']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_CALORIES"]["TABLE_FORMAT"])
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@ -170,13 +167,21 @@ for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys():
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files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_intraday.csv", pid=config["PIDS"]))
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for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys():
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if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"]))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"]))
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files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_features/fitbit_steps_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps.csv", pid=config["PIDS"]))
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for provider in config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"].keys():
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if config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_raw.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_summary.csv", pid=config["PIDS"]))
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for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys():
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if config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_raw.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_intraday.csv", pid=config["PIDS"]))
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for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys():
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if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]:
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34
config.yaml
34
config.yaml
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@ -277,31 +277,27 @@ FITBIT_HEARTRATE_INTRADAY:
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SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday
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SRC_LANGUAGE: "python"
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FITBIT_STEPS:
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TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES
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TABLE:
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JSON: fitbit_steps
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CSV:
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SUMMARY: steps_summary
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INTRADAY: steps_intraday
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EXCLUDE_SLEEP: # you can exclude sleep periods from the step features computation
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EXCLUDE: False
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TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section)
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FIXED:
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START: "23:00"
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END: "07:00"
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FITBIT_STEPS_SUMMARY:
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TABLE: steps_summary
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"]
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SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_summary
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SRC_LANGUAGE: "python"
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FITBIT_STEPS_INTRADAY:
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TABLE: steps_intraday
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES:
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SUMMARY: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"]
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INTRADAY:
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STEPS: ["sum", "max", "min", "avg", "std"]
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SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
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ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
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STEPS: ["sum", "max", "min", "avg", "std"]
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SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
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ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
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THRESHOLD_ACTIVE_BOUT: 10 # steps
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INCLUDE_ZERO_STEP_ROWS: False
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SRC_FOLDER: "rapids" # inside src/features/fitbit_steps
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SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday
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SRC_LANGUAGE: "python"
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FITBIT_SLEEP:
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@ -424,51 +424,57 @@ rule fitbit_heartrate_intraday_r_features:
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script:
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"../src/features/entry.R"
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rule fitbit_steps_python_features:
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rule fitbit_steps_summary_python_features:
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input:
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sensor_data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]),
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sensor_data = "data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv",
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day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
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params:
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provider = lambda wildcards: config["FITBIT_STEPS"]["PROVIDERS"][wildcards.provider_key.upper()],
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provider = lambda wildcards: config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()],
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provider_key = "{provider_key}",
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sensor_key = "fitbit_steps"
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sensor_key = "fitbit_steps_summary"
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output:
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"data/interim/{pid}/fitbit_steps_features/fitbit_steps_python_{provider_key}.csv"
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"data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_python_{provider_key}.csv"
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script:
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"../src/features/entry.py"
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rule fitbit_steps_r_features:
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rule fitbit_steps_summary_r_features:
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input:
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sensor_data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]),
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sensor_data = "data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv",
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day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
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params:
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provider = lambda wildcards: config["FITBIT_STEPS"]["PROVIDERS"][wildcards.provider_key.upper()],
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provider = lambda wildcards: config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()],
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provider_key = "{provider_key}",
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sensor_key = "fitbit_steps"
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sensor_key = "fitbit_steps_summary"
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output:
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"data/interim/{pid}/fitbit_steps_features/fitbit_steps_r_{provider_key}.csv"
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"data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_r_{provider_key}.csv"
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script:
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"../src/features/entry.R"
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# rule fitbit_step_features:
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# input:
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# step_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv",
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# sleep_data = optional_steps_sleep_input
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# params:
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# day_segment = "{day_segment}",
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# features_all_steps = config["STEP"]["FEATURES"]["ALL_STEPS"],
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# features_sedentary_bout = config["STEP"]["FEATURES"]["SEDENTARY_BOUT"],
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# features_active_bout = config["STEP"]["FEATURES"]["ACTIVE_BOUT"],
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# threshold_active_bout = config["STEP"]["THRESHOLD_ACTIVE_BOUT"],
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# include_zero_step_rows = config["STEP"]["INCLUDE_ZERO_STEP_ROWS"],
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# exclude_sleep = config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"],
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# exclude_sleep_type = config["STEP"]["EXCLUDE_SLEEP"]["TYPE"],
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# exclude_sleep_fixed_start = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["START"],
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# exclude_sleep_fixed_end = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["END"],
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# output:
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# "data/processed/{pid}/fitbit_step_{day_segment}.csv"
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# script:
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# "../src/features/fitbit_step_features.py"
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rule fitbit_steps_intraday_python_features:
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input:
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sensor_data = "data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv",
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day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
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params:
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provider = lambda wildcards: config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()],
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provider_key = "{provider_key}",
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sensor_key = "fitbit_steps_intraday"
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output:
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"data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_python_{provider_key}.csv"
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script:
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"../src/features/entry.py"
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rule fitbit_steps_intraday_r_features:
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input:
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sensor_data = "data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv",
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day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
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params:
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provider = lambda wildcards: config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()],
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provider_key = "{provider_key}",
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sensor_key = "fitbit_steps_intraday"
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output:
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"data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_r_{provider_key}.csv"
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script:
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"../src/features/entry.R"
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# rule fitbit_sleep_features:
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# input:
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@ -195,14 +195,14 @@ rule fitbit_parse_heartrate:
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rule fitbit_parse_steps:
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input:
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data = expand("data/raw/{{pid}}/fitbit_steps_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))
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"data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv"
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params:
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timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"],
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table = config["FITBIT_STEPS"]["TABLE"],
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table_format = config["FITBIT_STEPS"]["TABLE_FORMAT"]
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table = lambda wildcards: config["FITBIT_STEPS_"+str(wildcards.fitbit_data_type).upper()]["TABLE"],
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column_format = config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["COLUMN_FORMAT"],
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fitbit_data_type = "{fitbit_data_type}"
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output:
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summary_data = "data/raw/{pid}/fitbit_steps_summary_parsed.csv",
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intraday_data = "data/raw/{pid}/fitbit_steps_intraday_parsed.csv"
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"data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv"
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script:
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"../src/data/fitbit_parse_steps.py"
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@ -7,55 +7,65 @@ from math import trunc
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STEPS_COLUMNS = ("device_id", "steps", "local_date_time", "timestamp")
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def parseStepsData(steps_data):
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def parseStepsData(steps_data, fitbit_data_type):
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if steps_data.empty:
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return pd.DataFrame(), pd.DataFrame(columns=STEPS_INTRADAY_COLUMNS)
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device_id = steps_data["device_id"].iloc[0]
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records_summary, records_intraday = [], []
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# Parse JSON into individual records
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for record in steps_data.fitbit_data:
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record = json.loads(record) # Parse text into JSON
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curr_date = datetime.strptime(record["activities-steps"][0]["dateTime"], "%Y-%m-%d")
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# Parse summary data
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curr_date = datetime.strptime(
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record["activities-steps"][0]["dateTime"], "%Y-%m-%d")
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row_summary = (device_id,
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record["activities-steps"][0]["value"],
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curr_date,
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0)
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records_summary.append(row_summary)
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if fitbit_data_type == "summary":
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row_summary = (device_id,
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record["activities-steps"][0]["value"],
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curr_date,
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0)
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records_summary.append(row_summary)
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# Parse intraday data
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dataset = record["activities-steps-intraday"]["dataset"]
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for data in dataset:
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d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
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d_datetime = datetime.combine(curr_date, d_time)
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if fitbit_data_type == "intraday":
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dataset = record["activities-steps-intraday"]["dataset"]
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for data in dataset:
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d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
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d_datetime = datetime.combine(curr_date, d_time)
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row_intraday = (device_id,
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data["value"],
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d_datetime,
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0)
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row_intraday = (device_id,
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data["value"],
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d_datetime,
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0)
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records_intraday.append(row_intraday)
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if fitbit_data_type == "summary":
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parsed_data = pd.DataFrame(data=records_summary, columns=STEPS_COLUMNS)
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elif fitbit_data_type == "intraday":
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parsed_data = pd.DataFrame(data=records_intraday, columns=STEPS_COLUMNS)
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else:
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raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].")
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return parsed_data
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records_intraday.append(row_intraday)
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return pd.DataFrame(data=records_summary, columns=STEPS_COLUMNS), pd.DataFrame(data=records_intraday, columns=STEPS_COLUMNS)
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table_format = snakemake.params["table_format"]
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timezone = snakemake.params["timezone"]
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column_format = snakemake.params["column_format"]
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fitbit_data_type = snakemake.params["fitbit_data_type"]
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if table_format == "JSON":
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if column_format == "JSON":
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json_raw = pd.read_csv(snakemake.input[0])
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summary, intraday = parseStepsData(json_raw)
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elif table_format == "CSV":
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summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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parsed_data = parseStepsData(json_raw, fitbit_data_type)
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elif column_format == "PLAIN_TEXT":
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parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
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else:
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raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].")
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if summary.shape[0] > 0:
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summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
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if intraday.shape[0] > 0:
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intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
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if parsed_data.shape[0] > 0:
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parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
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summary.to_csv(snakemake.output["summary_data"], index=False)
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intraday.to_csv(snakemake.output["intraday_data"], index=False)
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parsed_data.to_csv(snakemake.output[0], index=False)
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@ -38,16 +38,6 @@ def getBouts(steps_data):
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return bouts
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def extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute):
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steps_summary_features = pd.DataFrame()
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# statistics features of daily steps count
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steps_summary_features = statsFeatures(steps_summary_data, summary_features_to_compute, "sumsteps", steps_summary_features)
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steps_summary_features.reset_index(inplace=True)
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return steps_summary_features
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def extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_bout, intraday_features_to_compute_steps, intraday_features_to_compute_sedentarybout, intraday_features_to_compute_activebout, steps_intraday_features):
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steps_intraday_features = pd.DataFrame()
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@ -73,22 +63,20 @@ def extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_b
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return steps_intraday_features
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def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||
|
||||
threshold_active_bout = provider["THRESHOLD_ACTIVE_BOUT"]
|
||||
include_zero_step_rows = provider["INCLUDE_ZERO_STEP_ROWS"]
|
||||
|
||||
steps_summary_data = pd.read_csv(sensor_data_files["sensor_data"][0])
|
||||
steps_intraday_data = pd.read_csv(sensor_data_files["sensor_data"][1])
|
||||
steps_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
|
||||
|
||||
requested_summary_features = ["summary" + x for x in provider["FEATURES"]["SUMMARY"]]
|
||||
requested_intraday_features = provider["FEATURES"]["INTRADAY"]
|
||||
requested_intraday_features = provider["FEATURES"]
|
||||
|
||||
requested_intraday_features_steps = ["intraday" + x + "steps" for x in requested_intraday_features["STEPS"]]
|
||||
requested_intraday_features_sedentarybout = ["intraday" + x + "sedentarybout" for x in requested_intraday_features["SEDENTARY_BOUT"]]
|
||||
requested_intraday_features_activebout = ["intraday" + x + "activebout" for x in requested_intraday_features["ACTIVE_BOUT"]]
|
||||
# name of the features this function can compute
|
||||
base_summary_features = ["summarymaxsumsteps", "summaryminsumsteps", "summaryavgsumsteps", "summarymediansumsteps", "summarystdsumsteps"]
|
||||
base_intraday_features_steps = ["intradaysumsteps", "intradaymaxsteps", "intradayminsteps", "intradayavgsteps", "intradaystdsteps"]
|
||||
base_intraday_features_sedentarybout = ["intradaycountepisodesedentarybout", "intradaysumdurationsedentarybout", "intradaymaxdurationsedentarybout", "intradaymindurationsedentarybout", "intradayavgdurationsedentarybout", "intradaystddurationsedentarybout"]
|
||||
base_intraday_features_activebout = ["intradaycountepisodeactivebout", "intradaysumdurationactivebout", "intradaymaxdurationactivebout", "intradaymindurationactivebout", "intradayavgdurationactivebout", "intradaystddurationactivebout"]
|
||||
|
|
@ -97,25 +85,8 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg
|
|||
intraday_features_to_compute_sedentarybout = list(set(requested_intraday_features_sedentarybout) & set(base_intraday_features_sedentarybout))
|
||||
intraday_features_to_compute_activebout = list(set(requested_intraday_features_activebout) & set(base_intraday_features_activebout))
|
||||
|
||||
summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features))
|
||||
intraday_features_to_compute = intraday_features_to_compute_steps + intraday_features_to_compute_sedentarybout + intraday_features_to_compute_activebout
|
||||
|
||||
# extract features from summary data
|
||||
steps_summary_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in summary_features_to_compute])
|
||||
if not steps_summary_data.empty:
|
||||
steps_summary_data = filter_data_by_segment(steps_summary_data, day_segment)
|
||||
|
||||
if not steps_summary_data.empty:
|
||||
# only keep the segments start at 00:00:00 and end at 23:59:59
|
||||
datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
|
||||
datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
|
||||
|
||||
segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex)
|
||||
steps_summary_data = steps_summary_data[steps_summary_data["local_segment"].str.match(segment_regex)]
|
||||
|
||||
if not steps_summary_data.empty:
|
||||
steps_summary_features = extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute)
|
||||
|
||||
# extract features from intraday features
|
||||
steps_intraday_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in intraday_features_to_compute])
|
||||
if not steps_intraday_data.empty:
|
||||
|
|
@ -124,18 +95,14 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg
|
|||
if not steps_intraday_data.empty:
|
||||
steps_intraday_features = extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_bout, intraday_features_to_compute_steps, intraday_features_to_compute_sedentarybout, intraday_features_to_compute_activebout, steps_intraday_features)
|
||||
|
||||
# merge summary features and intraday features
|
||||
steps_features = steps_intraday_features.merge(steps_summary_features, on=["local_segment"], how="outer")
|
||||
|
||||
|
||||
# exclude rows when the total step count is ZERO during the whole day
|
||||
if not include_zero_step_rows:
|
||||
steps_features.index = steps_features["local_segment"].apply(lambda segment: segment.split("#")[1][:10])
|
||||
steps_intraday_features.index = steps_intraday_features["local_segment"].apply(lambda segment: segment.split("#")[1][:10])
|
||||
|
||||
steps_features["dailycountstep"] = steps_intraday_data.groupby(["local_date"])["steps"].sum()
|
||||
steps_features = steps_features.query("dailycountstep != 0")
|
||||
steps_intraday_features["dailycountstep"] = steps_intraday_data.groupby(["local_date"])["steps"].sum()
|
||||
steps_intraday_features = steps_intraday_features.query("dailycountstep != 0")
|
||||
|
||||
del steps_features["dailycountstep"]
|
||||
steps_features.reset_index(drop=True, inplace=True)
|
||||
del steps_intraday_features["dailycountstep"]
|
||||
steps_intraday_features.reset_index(drop=True, inplace=True)
|
||||
|
||||
return steps_features
|
||||
return steps_intraday_features
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
def statsFeatures(steps_data, features_to_compute, features_type, steps_features):
|
||||
if features_type == "steps" or features_type == "sumsteps":
|
||||
col_name = "steps"
|
||||
elif features_type == "durationsedentarybout" or features_type == "durationactivebout":
|
||||
col_name = "duration"
|
||||
else:
|
||||
raise ValueError("features_type can only be one of ['steps', 'sumsteps', 'durationsedentarybout', 'durationactivebout'].")
|
||||
|
||||
if ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode") in features_to_compute:
|
||||
steps_features["steps_rapids_" + ("summarycount" if features_type == "sumsteps" else "intradaycount") + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count()
|
||||
if ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type in features_to_compute:
|
||||
steps_features["steps_rapids_" + ("summarysum" if features_type == "sumsteps" else "intradaysum") + features_type] = steps_data.groupby(["local_segment"])[col_name].sum()
|
||||
if ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type in features_to_compute:
|
||||
steps_features["steps_rapids_" + ("summarymax" if features_type == "sumsteps" else "intradaymax") + features_type] = steps_data.groupby(["local_segment"])[col_name].max()
|
||||
if ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type in features_to_compute:
|
||||
steps_features["steps_rapids_" + ("summarymin" if features_type == "sumsteps" else "intradaymin") + features_type] = steps_data.groupby(["local_segment"])[col_name].min()
|
||||
if ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type in features_to_compute:
|
||||
steps_features["steps_rapids_" + ("summaryavg" if features_type == "sumsteps" else "intradayavg") + features_type] = steps_data.groupby(["local_segment"])[col_name].mean()
|
||||
if ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type in features_to_compute:
|
||||
steps_features["steps_rapids_" + ("summarymedian" if features_type == "sumsteps" else "intradaymedian") + features_type] = steps_data.groupby(["local_segment"])[col_name].median()
|
||||
if ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type in features_to_compute:
|
||||
steps_features["steps_rapids_" + ("summarystd" if features_type == "sumsteps" else "intradaystd") + features_type] = steps_data.groupby(["local_segment"])[col_name].std()
|
||||
|
||||
return steps_features
|
||||
|
||||
def extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute):
|
||||
steps_summary_features = pd.DataFrame()
|
||||
|
||||
# statistics features of daily steps count
|
||||
steps_summary_features = statsFeatures(steps_summary_data, summary_features_to_compute, "sumsteps", steps_summary_features)
|
||||
|
||||
steps_summary_features.reset_index(inplace=True)
|
||||
|
||||
return steps_summary_features
|
||||
|
||||
|
||||
|
||||
def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||
|
||||
steps_summary_data = pd.read_csv(sensor_data_files["sensor_data"])
|
||||
requested_summary_features = ["summary" + x for x in provider["FEATURES"]]
|
||||
|
||||
# name of the features this function can compute
|
||||
base_summary_features = ["summarymaxsumsteps", "summaryminsumsteps", "summaryavgsumsteps", "summarymediansumsteps", "summarystdsumsteps"]
|
||||
# the subset of requested features this function can compute
|
||||
summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features))
|
||||
|
||||
# extract features from summary data
|
||||
steps_summary_features = pd.DataFrame(columns=["local_segment"] + ["steps_rapids_" + x for x in summary_features_to_compute])
|
||||
if not steps_summary_data.empty:
|
||||
steps_summary_data = filter_data_by_segment(steps_summary_data, day_segment)
|
||||
|
||||
if not steps_summary_data.empty:
|
||||
# only keep the segments start at 00:00:00 and end at 23:59:59
|
||||
datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
|
||||
datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
|
||||
|
||||
segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex)
|
||||
steps_summary_data = steps_summary_data[steps_summary_data["local_segment"].str.match(segment_regex)]
|
||||
|
||||
if not steps_summary_data.empty:
|
||||
steps_summary_features = extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute)
|
||||
|
||||
return steps_summary_features
|
||||
Loading…
Reference in New Issue