Add accelerometer features of RAPIDS provider
Meng Li
parent
55c12fc83c
commit
29dcd1f284
10
Snakefile
10
Snakefile
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@ -104,10 +104,12 @@ for provider in config["LIGHT"]["PROVIDERS"].keys():
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files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower()))
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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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for provider in config["ACCELEROMETER"]["PROVIDERS"].keys():
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if config["ACCELEROMETER"]["PROVIDERS"][provider]["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/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="ACCELEROMETER".lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="ACCELEROMETER".lower()))
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for provider in config["APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys():
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if config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]:
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14
config.yaml
14
config.yaml
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@ -158,16 +158,14 @@ LIGHT:
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SRC_FOLDER: "rapids" # inside src/features/light
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SRC_LANGUAGE: "python"
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ACCELEROMETER:
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COMPUTE: False
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DB_TABLE: accelerometer
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DAY_SEGMENTS: *day_segments
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FEATURES:
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MAGNITUDE: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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EXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
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NONEXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
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VALID_SENSED_MINUTES: False
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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SRC_FOLDER: "rapids" # inside src/features/accelerometer
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SRC_LANGUAGE: "python"
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APPLICATIONS_FOREGROUND:
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DB_TABLE: applications_foreground
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@ -28,19 +28,31 @@ rule resample_episodes_with_datetime:
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script:
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"../src/data/readable_datetime.R"
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rule accelerometer_features:
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rule accelerometer_r_features:
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input:
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expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"]),
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sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"])[0],
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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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day_segment = "{day_segment}",
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magnitude = config["ACCELEROMETER"]["FEATURES"]["MAGNITUDE"],
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exertional_activity_episode = config["ACCELEROMETER"]["FEATURES"]["EXERTIONAL_ACTIVITY_EPISODE"],
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nonexertional_activity_episode = config["ACCELEROMETER"]["FEATURES"]["NONEXERTIONAL_ACTIVITY_EPISODE"],
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valid_sensed_minutes = config["ACCELEROMETER"]["FEATURES"]["VALID_SENSED_MINUTES"],
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provider = lambda wildcards: config["ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key],
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provider_key = "{provider_key}",
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sensor_key = "accelerometer"
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output:
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"data/processed/{pid}/accelerometer_{day_segment}.csv"
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"data/interim/{pid}/accelerometer_features/accelerometer_r_{provider_key}.csv"
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script:
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"../src/features/accelerometer_features.py"
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"../src/features/entry.R"
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rule accelerometer_python_features:
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input:
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sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"])[0],
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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["ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key],
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provider_key = "{provider_key}",
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sensor_key = "accelerometer"
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output:
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"data/interim/{pid}/accelerometer_features/accelerometer_python_{provider_key}.csv"
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script:
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"../src/features/entry.py"
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rule activity_recognition_episodes:
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input:
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@ -0,0 +1,36 @@
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import pandas as pd
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import numpy as np
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def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
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acc_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_features = provider["FEATURES"]
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# name of the features this function can compute
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base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_rapids_" + x for x in features_to_compute])
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if not acc_data.empty:
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acc_data = filter_data_by_segment(acc_data, day_segment)
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if not acc_data.empty:
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acc_features = pd.DataFrame()
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# get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)
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magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1)
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acc_data = acc_data.assign(magnitude = magnitude.values)
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if "maxmagnitude" in features_to_compute:
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acc_features["acc_rapids_maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max()
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if "minmagnitude" in features_to_compute:
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acc_features["acc_rapids_minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min()
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if "avgmagnitude" in features_to_compute:
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acc_features["acc_rapids_avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean()
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if "medianmagnitude" in features_to_compute:
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acc_features["acc_rapids_medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median()
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if "stdmagnitude" in features_to_compute:
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acc_features["acc_rapids_stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std()
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acc_features = acc_features.reset_index()
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return acc_features
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