junos
/
rapids
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Update AR module for segments; Refactor input format

pull/103/head
Meng Li 2020-10-07 18:11:06 -04:00
parent 01ab59a3b6
commit 236b1cd809
43 changed files with 571 additions and 766 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
Snakefile
View File

@ -55,16 +55,22 @@ for provider in config["BLUETOOTH"]["PROVIDERS"].keys():
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BLUETOOTH".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BLUETOOTH".lower()))
if config["ACTIVITY_RECOGNITION"]["COMPUTE"]:
pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"]))
pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"]))
for provider in config["ACTIVITY_RECOGNITION"]["PROVIDERS"].keys():
if config["ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]:
pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"]))
pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"]))
for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]):
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes_resampled.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="ACTIVITY_RECOGNITION".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="ACTIVITY_RECOGNITION".lower()))
for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]):
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/processed/{pid}/{sensor}_deltas.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"]))
for provider in config["BATTERY"]["PROVIDERS"].keys():
if config["BATTERY"]["PROVIDERS"][provider]["COMPUTE"]:

13
config.yaml
View File

@ -113,12 +113,19 @@ BLUETOOTH:
ACTIVITY_RECOGNITION:
COMPUTE: False
DB_TABLE:
ANDROID: plugin_google_activity_recognition
IOS: plugin_ios_activity_recognition
DAY_SEGMENTS: *day_segments
FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"]
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"]
ACTIVITY_CLASSES:
STATIONARY: ["still", "tilting"]
MOBILE: ["on_foot", "walking", "running", "on_bicycle"]
VEHICLE: ["in_vehicle"]
SRC_FOLDER: "rapids" # inside src/features/activity_recognition
SRC_LANGUAGE: "python"
BATTERY:
DB_TABLE: battery

10
rules/common.smk
View File

@ -48,19 +48,17 @@ def optional_ar_input(wildcards):
platform = infer_participant_platform("data/external/"+wildcards.pid)
if platform == "android":
return ["data/raw/{pid}/" + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "_with_datetime_unified.csv",
"data/interim/{pid}/" + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "_episodes.csv"]
return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"])
elif platform == "ios":
return ["data/raw/{pid}/"+config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]+"_with_datetime_unified.csv",
"data/interim/{pid}/"+config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]+"_episodes.csv"]
return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"])
def optional_conversation_input(wildcards):
platform = infer_participant_platform("data/external/"+wildcards.pid)
if platform == "android":
return ["data/raw/{pid}/" + config["CONVERSATION"]["DB_TABLE"]["ANDROID"] + "_with_datetime_unified.csv"]
return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CONVERSATION"]["DB_TABLE"]["ANDROID"])[0]
elif platform == "ios":
return ["data/raw/{pid}/" + config["CONVERSATION"]["DB_TABLE"]["IOS"] + "_with_datetime_unified.csv"]
return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CONVERSATION"]["DB_TABLE"]["IOS"])[0]
def optional_steps_sleep_input(wildcards):
if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED":

522
rules/features.smk
View File

@ -6,70 +6,6 @@ rule join_features_from_providers:
script:
"../src/features/join_features_from_providers.R"
rule messages_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/messages_features/messages_r_{provider_key}.csv"
script:
"../src/features/messages/messages_entry.R"
rule messages_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/messages_features/messages_python_{provider_key}.csv"
script:
"../src/features/messages/messages_entry.py"
rule calls_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/calls_features/calls_python_{provider_key}.csv"
script:
"../src/features/calls/calls_entry.py"
rule calls_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/calls_features/calls_r_{provider_key}.csv"
script:
"../src/features/calls/calls_entry.R"
rule battery_episodes:
input:
expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor=config["BATTERY"]["DB_TABLE"])
output:
"data/interim/{pid}/battery_episodes.csv"
script:
"../src/features/battery/episodes/battery_episodes.R"
rule screen_episodes:
input:
screen = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["SCREEN"]["DB_TABLE"])
output:
"data/interim/{pid}/screen_episodes.csv"
script:
"../src/features/screen/episodes/screen_episodes.R"
rule resample_episodes:
input:
"data/interim/{pid}/{sensor}_episodes.csv"
@ -92,178 +28,6 @@ rule resample_episodes_with_datetime:
script:
"../src/data/readable_datetime.R"
rule google_activity_recognition_deltas:
input:
expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"])
output:
expand("data/interim/{{pid}}/{sensor}_episodes.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"])
script:
"../src/features/ar/episodes/activity_recognition_episodes.R"
rule ios_activity_recognition_deltas:
input:
expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"])
output:
expand("data/interim/{{pid}}/{sensor}_episodes.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"])
script:
"../src/features/ar/episodes/activity_recognition_episodes.R"
rule locations_python_features:
input:
sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
output:
"data/interim/{pid}/locations_features/locations_python_{provider_key}.csv"
script:
"../src/features/locations/locations_entry.py"
rule locations_r_features:
input:
sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/locations_features/locations_r_{provider_key}.csv"
script:
"../src/features/locations/locations_entry.R"
rule bluetooth_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/bluetooth_features/bluetooth_r_{provider_key}.csv"
script:
"../src/features/bluetooth/bluetooth_entry.R"
rule bluetooth_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/bluetooth_features/bluetooth_python_{provider_key}.csv"
script:
"../src/features/bluetooth/bluetooth_entry.py"
rule activity_features:
input:
optional_ar_input
params:
segment = "{day_segment}",
features = config["ACTIVITY_RECOGNITION"]["FEATURES"]
output:
"data/processed/{pid}/activity_recognition_{day_segment}.csv"
script:
"../src/features/activity_recognition.py"
rule battery_r_features:
input:
battery_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/battery_features/battery_r_{provider_key}.csv"
script:
"../src/features/battery/battery_entry.R"
rule battery_python_features:
input:
battery_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/battery_features/battery_python_{provider_key}.csv"
script:
"../src/features/battery/battery_entry.py"
rule screen_r_features:
input:
screen_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/screen_features/screen_r_{provider_key}.csv"
script:
"../src/features/screen/screen_entry.R"
rule screen_python_features:
input:
screen_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/screen_features/screen_python_{provider_key}.csv"
script:
"../src/features/screen/screen_entry.py"
rule light_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/light_features/light_r_{provider_key}.csv"
script:
"../src/features/light/light_entry.R"
rule light_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/light_features/light_python_{provider_key}.csv"
script:
"../src/features/light/light_entry.py"
rule conversation_r_features:
input:
sensor_data = optional_conversation_input,
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/conversation_features/conversation_r_{provider_key}.csv"
script:
"../src/features/conversation/conversation_entry.R"
rule conversation_python_features:
input:
sensor_data = optional_conversation_input,
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/conversation_features/conversation_python_{provider_key}.csv"
script:
"../src/features/conversation/conversation_entry.py"
rule accelerometer_features:
input:
expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"]),
@ -278,53 +42,315 @@ rule accelerometer_features:
script:
"../src/features/accelerometer_features.py"
rule activity_recognition_episodes:
input:
optional_ar_input
output:
"data/interim/{pid}/activity_recognition_episodes.csv"
script:
"../src/features/activity_recognition/episodes/activity_recognition_episodes.R"
rule activity_recognition_r_features:
input:
sensor_episodes = "data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "activity_recognition"
output:
"data/interim/{pid}/activity_recognition_features/activity_recognition_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule activity_recognition_python_features:
input:
sensor_episodes = "data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "activity_recognition"
output:
"data/interim/{pid}/activity_recognition_features/activity_recognition_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule applications_foreground_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]),
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
provider_key = "{provider_key}",
sensor_key = "applications_foreground"
output:
"data/interim/{pid}/applications_foreground_features/applications_foreground_r_{provider_key}.csv"
script:
"../src/features/applications_foreground/applications_foreground_entry.R"
"../src/features/entry.R"
rule applications_foreground_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]),
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
provider_key = "{provider_key}",
sensor_key = "applications_foreground"
output:
"data/interim/{pid}/applications_foreground_features/applications_foreground_python_{provider_key}.csv"
script:
"../src/features/applications_foreground/applications_foreground_entry.py"
"../src/features/entry.py"
rule battery_episodes:
input:
expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor=config["BATTERY"]["DB_TABLE"])
output:
"data/interim/{pid}/battery_episodes.csv"
script:
"../src/features/battery/episodes/battery_episodes.R"
rule battery_r_features:
input:
sensor_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "battery"
output:
"data/interim/{pid}/battery_features/battery_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule battery_python_features:
input:
sensor_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "battery"
output:
"data/interim/{pid}/battery_features/battery_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule bluetooth_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "bluetooth"
output:
"data/interim/{pid}/bluetooth_features/bluetooth_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule bluetooth_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "bluetooth"
output:
"data/interim/{pid}/bluetooth_features/bluetooth_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule calls_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "calls"
output:
"data/interim/{pid}/calls_features/calls_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule calls_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "calls"
output:
"data/interim/{pid}/calls_features/calls_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule conversation_r_features:
input:
sensor_data = optional_conversation_input,
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "conversation"
output:
"data/interim/{pid}/conversation_features/conversation_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule conversation_python_features:
input:
sensor_data = optional_conversation_input,
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "conversation"
output:
"data/interim/{pid}/conversation_features/conversation_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule light_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "light"
output:
"data/interim/{pid}/light_features/light_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule light_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "light"
output:
"data/interim/{pid}/light_features/light_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule locations_r_features:
input:
sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "locations"
output:
"data/interim/{pid}/locations_features/locations_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule locations_python_features:
input:
sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "locations"
output:
"data/interim/{pid}/locations_features/locations_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule messages_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "messages"
output:
"data/interim/{pid}/messages_features/messages_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule messages_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "messages"
output:
"data/interim/{pid}/messages_features/messages_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule screen_episodes:
input:
screen = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["SCREEN"]["DB_TABLE"])
output:
"data/interim/{pid}/screen_episodes.csv"
script:
"../src/features/screen/episodes/screen_episodes.R"
rule screen_r_features:
input:
sensor_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "screen"
output:
"data/interim/{pid}/screen_features/screen_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule screen_python_features:
input:
sensor_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "screen"
output:
"data/interim/{pid}/screen_features/screen_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule wifi_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower()),
sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower())[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
provider_key = "{provider_key}",
sensor_key = "wifi"
output:
"data/interim/{pid}/wifi_features/wifi_r_{provider_key}.csv"
script:
"../src/features/wifi/wifi_entry.R"
"../src/features/entry.R"
rule wifi_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower()),
sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower())[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
provider_key = "{provider_key}",
sensor_key = "wifi"
output:
"data/interim/{pid}/wifi_features/wifi_python_{provider_key}.csv"
script:
"../src/features/wifi/wifi_entry.py"
"../src/features/entry.py"
rule fitbit_heartrate_features:
input:

15
src/features/activity_recognition.py
View File

@ -1,15 +0,0 @@
import pandas as pd
from ar.ar_base import base_ar_features
ar_data = pd.read_csv(snakemake.input[0],parse_dates=["local_date_time"])
ar_deltas = pd.read_csv(snakemake.input[1],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
day_segment = snakemake.params["segment"]
requested_features = snakemake.params["features"]
ar_features = pd.DataFrame(columns=["local_date"])
ar_features = ar_features.merge(base_ar_features(ar_data, ar_deltas, day_segment, requested_features), on="local_date", how="outer")
assert len(requested_features) + 1 == ar_features.shape[1], "The number of features in the output dataframe (=" + str(ar_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your activity recognition feature extraction functions"
ar_features.to_csv(snakemake.output[0], index=False)

0
src/features/ar/episodes/activity_recognition_episodes.R → src/features/activity_recognition/episodes/activity_recognition_episodes.R
View File

123
src/features/activity_recognition/rapids/main.py 100644
View File

@ -0,0 +1,123 @@
import pandas as pd
import numpy as np
def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
chunk_episodes = kwargs["chunk_episodes"]
ar_episodes = pd.read_csv(sensor_data_files["sensor_episodes"])
activity_classes = provider["ACTIVITY_CLASSES"]
# name of the features this function can compute
base_features_names = ["count","mostcommonactivity","countuniqueactivities","durationstationary","durationmobile","durationvehicle"]
# the subset of requested features this function can compute
requested_features = provider["FEATURES"]
features_to_compute = list(set(requested_features) & set(base_features_names))
ar_features = pd.DataFrame(columns=["local_segment"] + ["ar_rapids_" + x for x in features_to_compute])
if not ar_episodes.empty:
ar_episodes = filter_data_by_segment(ar_episodes, day_segment)
if not ar_episodes.empty:
# chunk episodes
ar_episodes = chunk_episodes(ar_episodes)
if not ar_episodes.empty:
ar_features = pd.DataFrame()
if "count" in features_to_compute:
ar_features["ar_rapids_count"] = ar_episodes.groupby(["local_segment"]).count()["episode_id"]
if "mostcommonactivity" in features_to_compute:
ar_features["ar_rapids_mostcommonactivity"] = ar_episodes.groupby(["local_segment"])["activity_type"].agg(lambda x: pd.Series.mode(x)[0])
if "countuniqueactivities" in features_to_compute:
ar_features["ar_rapids_countuniqueactivities"] = ar_episodes.groupby(["local_segment"])["activity_type"].nunique()
# duration features
for column, activity_labels in activity_classes.items():
if "duration" + column.lower() in features_to_compute:
filtered_data = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))]
if not filtered_data.empty:
ar_features["ar_rapids_duration_" + column] = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))].groupby(["local_segment"])["duration"].sum().fillna(0)
else:
ar_features["ar_rapids_duration_" + column] = 0
ar_features.index.names = ["local_segment"]
ar_features = ar_features.reset_index()
return ar_features
"""
if not ar_data.empty:
ar_data = filter_data_by_segment(ar_data, day_segment)
if not ar_data.empty:
# chunk_episodes
ar_data = chunk_episodes(ar_data)
if not ar_data.empty:
ar_data["episode_id"] = ((ar_data.ar_status != ar_data.ar_status.shift()) | (ar_data.start_timestamp - ar_data.end_timestamp.shift() > 1)).cumsum()
grouped = ar_data.groupby(by=["local_segment", "episode_id", "ar_status"])
ar_episodes= grouped[["duration"]].sum()
ar_episodes["ar_diff"] = grouped["ar_level"].first() - grouped["ar_level"].last()
ar_episodes["ar_consumption_rate"] = ar_episodes["ar_diff"] / ar_episodes["duration"]
ar_episodes.reset_index(inplace=True)
# for discharge episodes
ar_discharge_episodes = ar_episodes[(ar_episodes["ar_status"] == 3) | (ar_episodes["ar_status"] == 4)]
ar_discharge_features = pd.DataFrame()
if "countdischarge" in features_to_compute:
ar_discharge_features["ar_rapids_countdischarge"] = ar_discharge_episodes.groupby(["local_segment"])["episode_id"].count()
if "sumdurationdischarge" in features_to_compute:
ar_discharge_features["ar_rapids_sumdurationdischarge"] = ar_discharge_episodes.groupby(["local_segment"])["duration"].sum()
if "avgconsumptionrate" in features_to_compute:
ar_discharge_features["ar_rapids_avgconsumptionrate"] = ar_discharge_episodes.groupby(["local_segment"])["ar_consumption_rate"].mean()
if "maxconsumptionrate" in features_to_compute:
ar_discharge_features["ar_rapids_maxconsumptionrate"] = ar_discharge_episodes.groupby(["local_segment"])["ar_consumption_rate"].max()
# for charge episodes
ar_charge_episodes = ar_episodes[(ar_episodes["ar_status"] == 2) | (ar_episodes["ar_status"] == 5)]
ar_charge_features = pd.DataFrame()
if "countcharge" in features_to_compute:
ar_charge_features["ar_rapids_countcharge"] = ar_charge_episodes.groupby(["local_segment"])["episode_id"].count()
if "sumdurationcharge" in features_to_compute:
ar_charge_features["ar_rapids_sumdurationcharge"] = ar_charge_episodes.groupby(["local_segment"])["duration"].sum()
# combine discharge features and charge features; fill the missing values with ZERO
ar_features = pd.concat([ar_discharge_features, ar_charge_features], axis=1, sort=True).fillna(0)
ar_features.index.rename("local_segment", inplace=True)
ar_features = ar_features.reset_index()
return ar_features
"""

13
src/features/applications_foreground/applications_foreground_entry.R
View File

@ -1,13 +0,0 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["sensor_data"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "applications_foreground", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/applications_foreground/applications_foreground_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
sensor_data_file = snakemake.input["sensor_data"][0]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "applications_foreground", sensor_data_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

25
src/features/applications_foreground/rapids/main.py
View File

@ -9,28 +9,31 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features
if "timeoffirstuse" in requested_features:
time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment")
if time_first_event.empty:
apps_features["apps_rapids" + "_timeoffirstuse" + apps_type] = np.nan
apps_features["apps_rapids_timeoffirstuse" + apps_type] = np.nan
else:
apps_features["apps_rapids" + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
apps_features["apps_rapids_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
if "timeoflastuse" in requested_features:
time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment")
if time_last_event.empty:
apps_features["apps_rapids" + "_timeoflastuse" + apps_type] = np.nan
apps_features["apps_rapids_timeoflastuse" + apps_type] = np.nan
else:
apps_features["apps_rapids" + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
apps_features["apps_rapids_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
if "frequencyentropy" in requested_features:
apps_with_count = filtered_data.groupby(["local_segment","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
if (len(apps_with_count.index) < 2 ):
apps_features["apps_rapids" + "_frequencyentropy" + apps_type] = np.nan
apps_features["apps_rapids_frequencyentropy" + apps_type] = np.nan
else:
apps_features["apps_rapids" + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy)
apps_features["apps_rapids_frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy)
if "count" in requested_features:
apps_features["apps_rapids" + "_count" + apps_type] = filtered_data.groupby(["local_segment"]).count()["timestamp"]
apps_features.fillna(value={"apps_rapids" + "_count" + apps_type: 0}, inplace=True)
apps_features["apps_rapids_count" + apps_type] = filtered_data.groupby(["local_segment"]).count()["timestamp"]
apps_features.fillna(value={"apps_rapids_count" + apps_type: 0}, inplace=True)
return apps_features
def rapids_features(apps_data, day_segment, provider, filter_data_by_segment, *args, **kwargs):
def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
apps_data = pd.read_csv(sensor_data_files["sensor_data"])
requested_features = provider["FEATURES"]
excluded_categories = provider["EXCLUDED_CATEGORIES"]
excluded_apps = provider["EXCLUDED_APPS"]
@ -50,9 +53,7 @@ def rapids_features(apps_data, day_segment, provider, filter_data_by_segment, *a
# exclude apps in the excluded_apps list
apps_data = apps_data[~apps_data["package_name"].isin(excluded_apps)]
apps_features = pd.DataFrame(columns=["local_segment"] + ["apps_rapids_" + "_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]])
apps_features = pd.DataFrame(columns=["local_segment"] + ["apps_rapids_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]])
if not apps_data.empty:
# deep copy the apps_data for the top1global computation
apps_data_global = apps_data.copy()

63
src/features/ar/ar_base.py
View File

@ -1,63 +0,0 @@
import pandas as pd
import numpy as np
import scipy.stats as stats
from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
def base_ar_features(ar_data, ar_deltas, day_segment, requested_features):
# name of the features this function can compute
base_features_names = ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"]
# the subset of requested features this function can compute
features_to_compute = list(set(requested_features) & set(base_features_names))
ar_features = pd.DataFrame(columns = ["local_date"] + ["ar_" + day_segment + "_" + x for x in features_to_compute])
if not ar_data.empty:
ar_deltas = splitOvernightEpisodes(ar_deltas, [],["activity"])
if day_segment != "daily":
ar_deltas = splitMultiSegmentEpisodes(ar_deltas, day_segment, [])
ar_data.local_date_time = pd.to_datetime(ar_data.local_date_time)
resampledData = ar_data.set_index(ar_data.local_date_time)
resampledData.drop(columns=["local_date_time"], inplace=True)
if day_segment != "daily":
resampledData = resampledData.loc[resampledData["local_day_segment"] == day_segment]
if not resampledData.empty:
ar_features = pd.DataFrame()
# finding the count of samples of the day
if "count" in features_to_compute:
ar_features["ar_" + day_segment + "_count"] = resampledData["activity_type"].resample("D").count()
# finding most common activity of the day
if "mostcommonactivity" in features_to_compute:
ar_features["ar_" + day_segment + "_mostcommonactivity"] = resampledData["activity_type"].resample("D").apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None)
# finding different number of activities during a day
if "countuniqueactivities" in features_to_compute:
ar_features["ar_" + day_segment + "_countuniqueactivities"] = resampledData["activity_type"].resample("D").nunique()
# finding Number of times activity changed
if "activitychangecount" in features_to_compute:
resampledData["activity_type_shift"] = resampledData["activity_type"].shift().fillna(resampledData["activity_type"].head(1))
resampledData["different_activity"] = np.where(resampledData["activity_type"]!=resampledData["activity_type_shift"],1,0)
ar_features["ar_" + day_segment + "_activitychangecount"] = resampledData["different_activity"].resample("D").sum()
deltas_features = {"sumstationary":["still","tilting"],
"summobile":["on_foot","walking","running","on_bicycle"],
"sumvehicle":["in_vehicle"]}
for column, activity_labels in deltas_features.items():
if column in features_to_compute:
filtered_data = ar_deltas[ar_deltas["activity"].isin(pd.Series(activity_labels))]
if not filtered_data.empty:
ar_features["ar_" + day_segment + "_" + column] = ar_deltas[ar_deltas["activity"].isin(pd.Series(activity_labels))].groupby(["local_start_date"])["time_diff"].sum().fillna(0)
else:
ar_features["ar_" + day_segment + "_" + column] = 0
ar_features.index.names = ["local_date"]
ar_features = ar_features.reset_index()
return ar_features

13
src/features/battery/battery_entry.R
View File

@ -1,13 +0,0 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["battery_episodes"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "battery", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/battery/battery_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
battery_episodes_file = snakemake.input["battery_episodes"]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "battery", battery_episodes_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

3
src/features/battery/rapids/main.py
View File

@ -1,8 +1,9 @@
import pandas as pd
from datetime import datetime, timedelta, time
def rapids_features(battery_data, day_segment, provider, filter_data_by_segment, *args, **kwargs):
def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
battery_data = pd.read_csv(sensor_data_files["sensor_episodes"])
chunk_episodes = kwargs["chunk_episodes"]
# name of the features this function can compute

13
src/features/battery_features.py
View File

@ -1,13 +0,0 @@
import pandas as pd
from battery.battery_base import base_battery_features
battery_data = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
day_segment = snakemake.params["day_segment"]
requested_features = snakemake.params["features"]
battery_features = pd.DataFrame(columns=["local_date"])
battery_features = battery_features.merge(base_battery_features(battery_data, day_segment, requested_features), on="local_date", how="outer")
assert len(requested_features) + 1 == battery_features.shape[1], "The number of features in the output dataframe (=" + str(battery_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your battery feature extraction functions"
battery_features.to_csv(snakemake.output[0], index=False)

13
src/features/bluetooth/bluetooth_entry.R
View File

@ -1,13 +0,0 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["sensor_data"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "bluetooth", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/bluetooth/bluetooth_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
sensor_data_file = snakemake.input["sensor_data"][0]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "bluetooth", sensor_data_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

30
src/features/bluetooth/rapids/main.R
View File

@ -27,24 +27,26 @@ compute_bluetooth_feature <- function(data, feature, day_segment){
}
}
rapids_features <- function(bluetooth_data, day_segment, provider){
requested_features <- provider[["FEATURES"]]
rapids_features <- function(sensor_data_files, day_segment, provider){
# Output dataframe
features = data.frame(local_segment = character(), stringsAsFactors = FALSE)
bluetooth_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
requested_features <- provider[["FEATURES"]]
# The name of the features this function can compute
base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice")
# Output dataframe
features = data.frame(local_segment = character(), stringsAsFactors = FALSE)
# The subset of requested features this function can compute
features_to_compute <- intersect(base_features_names, requested_features)
# The name of the features this function can compute
base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice")
for(feature_name in features_to_compute){
feature <- compute_bluetooth_feature(bluetooth_data, feature_name, day_segment)
features <- merge(features, feature, by="local_segment", all = TRUE)
}
# The subset of requested features this function can compute
features_to_compute <- intersect(base_features_names, requested_features)
features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0)))
for(feature_name in features_to_compute){
feature <- compute_bluetooth_feature(bluetooth_data, feature_name, day_segment)
features <- merge(features, feature, by="local_segment", all = TRUE)
}
return(features)
features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0)))
return(features)
}

18
src/features/calls/calls_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
sensor_data_file = snakemake.input["sensor_data"][0]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "calls", sensor_data_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

7
src/features/calls/rapids/main.R
View File

@ -62,8 +62,9 @@ call_features_of_type <- function(calls, call_type, day_segment, requested_featu
return(features)
}
rapids_features <- function(calls, day_segment, provider){
calls <- calls %>% filter_data_by_segment(day_segment)
rapids_features <- function(sensor_data_files, day_segment, provider){
calls_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
calls_data <- calls_data %>% filter_data_by_segment(day_segment)
call_types = provider[["CALL_TYPES"]]
call_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment"))
@ -74,7 +75,7 @@ rapids_features <- function(calls, day_segment, provider){
stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type, " in config[CALLS][CALL_TYPES]"))
requested_features <- provider[["FEATURES"]][[call_type]]
calls_of_type <- calls %>% filter(call_type == call_type_label)
calls_of_type <- calls_data %>% filter(call_type == call_type_label)
features <- call_features_of_type(calls_of_type, call_type, day_segment, requested_features)
call_features <- merge(call_features, features, all=TRUE)

13
src/features/conversation/conversation_entry.R
View File

@ -1,13 +0,0 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["sensor_data"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "conversation", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/conversation/conversation_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
sensor_data_file = snakemake.input["sensor_data"][0]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "conversation", sensor_data_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

71
src/features/conversation/rapids/main.py
View File

@ -1,8 +1,9 @@
import pandas as pd
import numpy as np
# def rapids_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes):
def rapids_features(conversation_data, day_segment, provider, filter_data_by_segment, *args, **kwargs):
def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
conversation_data = pd.read_csv(sensor_data_files["sensor_data"])
requested_features = provider["FEATURES"]
recordingMinutes = provider["RECORDING_MINUTES"]
@ -20,7 +21,7 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg
# the subset of requested features this function can compute
features_to_compute = list(set(requested_features) & set(base_features_names))
conversation_features = pd.DataFrame(columns=["local_segment"] + ["conversation_rapids" + "_" + x for x in features_to_compute])
conversation_features = pd.DataFrame(columns=["local_segment"] + ["conversation_rapids_" + x for x in features_to_compute])
if not conversation_data.empty:
conversation_data = filter_data_by_segment(conversation_data, day_segment)
@ -30,19 +31,19 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg
conversation_data = conversation_data.drop_duplicates(subset=["local_date", "local_time"], keep="first")
if "minutessilence" in features_to_compute:
conversation_features["conversation_rapids" + "_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60
conversation_features["conversation_rapids_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60
if "minutesnoise" in features_to_compute:
conversation_features["conversation_rapids" + "_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60
conversation_features["conversation_rapids_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60
if "minutesvoice" in features_to_compute:
conversation_features["conversation_rapids" + "_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60
conversation_features["conversation_rapids_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60
if "minutesunknown" in features_to_compute:
conversation_features["conversation_rapids" + "_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60
conversation_features["conversation_rapids_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60
if "countconversation" in features_to_compute:
conversation_features["conversation_rapids" + "_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['double_convo_start'].nunique()
conversation_features["conversation_rapids_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['double_convo_start'].nunique()
conv_duration = (conversation_data['double_convo_end']/1000 - conversation_data['double_convo_start']/1000)/60
conversation_data = conversation_data.assign(conv_duration = conv_duration.values)
@ -50,43 +51,43 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg
conv_totalDuration = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_segment"])['inference'].count()/60
if "silencesensedfraction" in features_to_compute:
conversation_features["conversation_rapids" + "_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
conversation_features["conversation_rapids_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
if "noisesensedfraction" in features_to_compute:
conversation_features["conversation_rapids" + "_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
conversation_features["conversation_rapids_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
if "voicesensedfraction" in features_to_compute:
conversation_features["conversation_rapids" + "_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
conversation_features["conversation_rapids_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
if "unknownsensedfraction" in features_to_compute:
conversation_features["conversation_rapids" + "_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
conversation_features["conversation_rapids_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
if "silenceexpectedfraction" in features_to_compute:
conversation_features["conversation_rapids" + "_silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
conversation_features["conversation_rapids_silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
if "noiseexpectedfraction" in features_to_compute:
conversation_features["conversation_rapids" + "_noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
conversation_features["conversation_rapids_noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
if "voiceexpectedfraction" in features_to_compute:
conversation_features["conversation_rapids" + "_voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
conversation_features["conversation_rapids_voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
if "unknownexpectedfraction" in features_to_compute:
conversation_features["conversation_rapids" + "_unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
conversation_features["conversation_rapids_unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
if "sumconversationduration" in features_to_compute:
conversation_features["conversation_rapids" + "_sumconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].sum()
conversation_features["conversation_rapids_sumconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].sum()
if "avgconversationduration" in features_to_compute:
conversation_features["conversation_rapids" + "_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].mean()
conversation_features["conversation_rapids_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].mean()
if "sdconversationduration" in features_to_compute:
conversation_features["conversation_rapids" + "_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].std()
conversation_features["conversation_rapids_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].std()
if "minconversationduration" in features_to_compute:
conversation_features["conversation_rapids" + "_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].min()
conversation_features["conversation_rapids_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].min()
if "maxconversationduration" in features_to_compute:
conversation_features["conversation_rapids" + "_maxconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].max()
conversation_features["conversation_rapids_maxconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].max()
if "timefirstconversation" in features_to_compute:
timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].min()
@ -94,9 +95,9 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg
for date in list(timestampsLastConversation.index):
lastimestamp = timestampsLastConversation.loc[date]
lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0]
conversation_features.loc[date,"conversation_rapids" + "_timefirstconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1])
conversation_features.loc[date,"conversation_rapids_timefirstconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1])
else:
conversation_features["conversation_rapids" + "_timefirstconversation"] = np.nan
conversation_features["conversation_rapids_timefirstconversation"] = np.nan
if "timelastconversation" in features_to_compute:
timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].max()
@ -104,39 +105,39 @@ def rapids_features(conversation_data, day_segment, provider, filter_data_by_seg
for date in list(timestampsLastConversation.index):
lastimestamp = timestampsLastConversation.loc[date]
lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0]
conversation_features.loc[date,"conversation_rapids" + "_timelastconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1])
conversation_features.loc[date,"conversation_rapids_timelastconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1])
else:
conversation_features["conversation_rapids" + "_timelastconversation"] = np.nan
conversation_features["conversation_rapids_timelastconversation"] = np.nan
if "noisesumenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].sum()
conversation_features["conversation_rapids_noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].sum()
if "noiseavgenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].mean()
conversation_features["conversation_rapids_noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].mean()
if "noisesdenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].std()
conversation_features["conversation_rapids_noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].std()
if "noiseminenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].min()
conversation_features["conversation_rapids_noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].min()
if "noisemaxenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].max()
conversation_features["conversation_rapids_noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].max()
if "voicesumenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].sum()
conversation_features["conversation_rapids_voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].sum()
if "voiceavgenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].mean()
conversation_features["conversation_rapids_voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].mean()
if "voicesdenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].std()
conversation_features["conversation_rapids_voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].std()
if "voiceminenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].min()
conversation_features["conversation_rapids_voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].min()
if "voicemaxenergy" in features_to_compute:
conversation_features["conversation_rapids" + "_voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].max()
conversation_features["conversation_rapids_voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].max()
conversation_features = conversation_features.reset_index()

9
src/features/calls/calls_entry.R → src/features/entry.R
View File

@ -3,11 +3,14 @@ source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["sensor_data"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
sensor_data_files <- snakemake@input
sensor_data_files$day_segments_labels <- NULL
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_key <- snakemake@params["sensor_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "calls", sensor_data_file, day_segments_file)
sensor_features <- fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

14
src/features/entry.py 100644
View File

@ -0,0 +1,14 @@
import pandas as pd
from utils.utils import fetch_provider_features
sensor_data_files = dict(snakemake.input)
del sensor_data_files["day_segments_labels"]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_key = snakemake.params["sensor_key"]
sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

13
src/features/light/light_entry.R
View File

@ -1,13 +0,0 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["sensor_data"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "light", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/light/light_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
sensor_data_file = snakemake.input["sensor_data"][0]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "light", sensor_data_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

18
src/features/light/rapids/main.py
View File

@ -1,33 +1,35 @@
import pandas as pd
import numpy as np
def rapids_features(light_data, day_segment, provider, filter_data_by_segment, *args, **kwargs):
def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
light_data = pd.read_csv(sensor_data_files["sensor_data"])
requested_features = provider["FEATURES"]
# name of the features this function can compute
base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
# the subset of requested features this function can compute
features_to_compute = list(set(requested_features) & set(base_features_names))
light_features = pd.DataFrame(columns=["local_segment"] + ["light_rapids_" + "_" + x for x in features_to_compute])
light_features = pd.DataFrame(columns=["local_segment"] + ["light_rapids_" + x for x in features_to_compute])
if not light_data.empty:
light_data = filter_data_by_segment(light_data, day_segment)
if not light_data.empty:
light_features = pd.DataFrame()
if "count" in features_to_compute:
light_features["light_rapids_" + "_count"] = light_data.groupby(["local_segment"]).count()["timestamp"]
light_features["light_rapids_count"] = light_data.groupby(["local_segment"]).count()["timestamp"]
# get light ambient luminance related features
if "maxlux" in features_to_compute:
light_features["light_rapids_" + "_maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max()
light_features["light_rapids_maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max()
if "minlux" in features_to_compute:
light_features["light_rapids_" + "_minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min()
light_features["light_rapids_minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min()
if "avglux" in features_to_compute:
light_features["light_rapids_" + "_avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean()
light_features["light_rapids_avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean()
if "medianlux" in features_to_compute:
light_features["light_rapids_" + "_medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median()
light_features["light_rapids_medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median()
if "stdlux" in features_to_compute:
light_features["light_rapids_" + "_stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std()
light_features["light_rapids_stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std()
light_features = light_features.reset_index()

5
src/features/locations/barnett/main.R
View File

@ -27,8 +27,11 @@ create_empty_file <- function(requested_features){
) %>% select(all_of(requested_features)))
}
barnett_features <- function(location_data, day_segment, params){
barnett_features <- function(sensor_data_files, day_segment, params){
location_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
location_features <- NULL
location <- location_data
accuracy_limit <- params[["ACCURACY_LIMIT"]]
timezone <- params[["TIMEZONE"]]

4
src/features/locations/doryab/main.py
View File

@ -4,7 +4,9 @@ from astropy.timeseries import LombScargle
from sklearn.cluster import DBSCAN
from math import radians, cos, sin, asin, sqrt
def doryab_features(location_data, day_segment, provider, filter_data_by_segment, *args, **kwargs):
def doryab_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
location_data = pd.read_csv(sensor_data_files["sensor_data"])
requested_features = provider["FEATURES"]
dbscan_eps = provider["DBSCAN_EPS"]
dbscan_minsamples = provider["DBSCAN_MINSAMPLES"]

13
src/features/locations/locations_entry.R
View File

@ -1,13 +0,0 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["sensor_data"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "locations", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/locations/locations_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
sensor_data_file = snakemake.input["sensor_data"][0]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "locations", sensor_data_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

13
src/features/messages/messages_entry.R
View File

@ -1,13 +0,0 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["sensor_data"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "messages", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/messages/messages_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
sensor_data_file = snakemake.input["sensor_data"][0]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "messages", sensor_data_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

7
src/features/messages/rapids/main.R
View File

@ -50,8 +50,9 @@ message_features_of_type <- function(messages, messages_type, day_segment, reque
return(features)
}
rapids_features <- function(messages, day_segment, provider){
messages <- messages %>% filter_data_by_segment(day_segment)
rapids_features <- function(sensor_data_files, day_segment, provider){
messages_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
messages_data <- messages_data %>% filter_data_by_segment(day_segment)
messages_types = provider[["MESSAGES_TYPES"]]
messages_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment"))
@ -62,7 +63,7 @@ rapids_features <- function(messages, day_segment, provider){
stop(paste("Message type can online be received or sent but instead you typed: ", message_type, " in config[MESSAGES][MESSAGES_TYPES]"))
requested_features <- provider[["FEATURES"]][[message_type]]
messages_of_type <- messages %>% filter(message_type == message_type_label)
messages_of_type <- messages_data %>% filter(message_type == message_type_label)
features <- message_features_of_type(messages_of_type, message_type, day_segment, requested_features)
messages_features <- merge(messages_features, features, all=TRUE)

4
src/features/screen/rapids/main.py
View File

@ -25,7 +25,9 @@ def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, refe
return duration_helper
def rapids_features(screen_data, day_segment, provider, filter_data_by_segment, *args, **kwargs):
def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
screen_data = pd.read_csv(sensor_data_files["sensor_episodes"])
reference_hour_first_use = provider["REFERENCE_HOUR_FIRST_USE"]
requested_features_episodes = provider["FEATURES"]

13
src/features/screen/screen_entry.R
View File

@ -1,13 +0,0 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["screen_episodes"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "screen", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/screen/screen_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
screen_episodes_file = snakemake.input["screen_episodes"]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "screen", screen_episodes_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

11
src/features/utils/utils.R
View File

@ -43,24 +43,23 @@ chunk_episodes <- function(sensor_episodes){
return(chunked_episodes)
}
fetch_provider_features <- function(provider, provider_key, config_key, sensor_data_file, day_segments_file){
fetch_provider_features <- function(provider, provider_key, sensor_key, sensor_data_files, day_segments_file){
sensor_features <- data.frame(local_segment = character(), stringsAsFactors = FALSE)
sensor_data <- read.csv(sensor_data_file, stringsAsFactors = FALSE)
day_segments_labels <- read.csv(day_segments_file, stringsAsFactors = FALSE)
if(!"FEATURES" %in% names(provider))
stop(paste0("Provider config[", config_key,"][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml"))
stop(paste0("Provider config[", sensor_key,"][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml"))
if(provider[["COMPUTE"]] == TRUE){
code_path <- paste0("src/features/", config_key,"/", provider[["SRC_FOLDER"]], "/main.R")
code_path <- paste0("src/features/", sensor_key,"/", provider[["SRC_FOLDER"]], "/main.R")
source(code_path)
features_function <- match.fun(paste0(provider[["SRC_FOLDER"]], "_features"))
day_segments <- day_segments_labels %>% pull(label)
for (day_segment in day_segments){
print(paste(rapids_log_tag,"Processing", config_key, provider_key, day_segment))
print(paste(rapids_log_tag,"Processing", sensor_key, provider_key, day_segment))
features <- features_function(sensor_data, day_segment, provider)
features <- features_function(sensor_data_files, day_segment, provider)
# Check all features names contain the provider key so they are unique
features_names <- colnames(features %>% select(-local_segment))

12
src/features/utils/utils.py
View File

@ -67,24 +67,24 @@ def chunk_episodes(sensor_episodes):
return merged_sensor_episodes
def fetch_provider_features(provider, provider_key, config_key, sensor_data_file, day_segments_file):
def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, day_segments_file):
import pandas as pd
from importlib import import_module, util
sensor_features = pd.DataFrame(columns=["local_segment"])
sensor_data = pd.read_csv(sensor_data_file)
day_segments_labels = pd.read_csv(day_segments_file, header=0)
if "FEATURES" not in provider:
raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(config_key.upper(), provider_key))
raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key))
if provider["COMPUTE"] == True:
code_path = provider["SRC_FOLDER"] + ".main"
code_path = sensor_key + "." + provider["SRC_FOLDER"] + ".main"
feature_module = import_module(code_path)
feature_function = getattr(feature_module, provider["SRC_FOLDER"] + "_features")
for day_segment in day_segments_labels["label"]:
print("{} Processing {} {} {}".format(rapids_log_tag, config_key, provider_key, day_segment))
features = feature_function(sensor_data, day_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes)
print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, day_segment))
features = feature_function(sensor_data_files, day_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes)
sensor_features = sensor_features.merge(features, how="outer")
else:
for feature in provider["FEATURES"]:

27
src/features/wifi/rapids/main.R
View File

@ -25,21 +25,22 @@ compute_wifi_feature <- function(data, feature, day_segment){
}
}
rapids_features <- function(wifi_data, day_segment, provider){
requested_features <- provider[["FEATURES"]]
# Output dataframe
features = data.frame(local_segment = character(), stringsAsFactors = FALSE)
rapids_features <- function(sensor_data_files, day_segment, provider){
wifi_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
requested_features <- provider[["FEATURES"]]
# Output dataframe
features = data.frame(local_segment = character(), stringsAsFactors = FALSE)
# The name of the features this function can compute
base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice")
# The name of the features this function can compute
base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice")
# The subset of requested features this function can compute
features_to_compute <- intersect(base_features_names, requested_features)
# The subset of requested features this function can compute
features_to_compute <- intersect(base_features_names, requested_features)
for(feature_name in features_to_compute){
feature <- compute_wifi_feature(wifi_data, feature_name, day_segment)
features <- merge(features, feature, by="local_segment", all = TRUE)
}
for(feature_name in features_to_compute){
feature <- compute_wifi_feature(wifi_data, feature_name, day_segment)
features <- merge(features, feature, by="local_segment", all = TRUE)
}
return(features)
return(features)
}

13
src/features/wifi/wifi_entry.R
View File

@ -1,13 +0,0 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["sensor_data"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "wifi", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/wifi/wifi_entry.py
View File

@ -1,18 +0,0 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
sensor_data_file = snakemake.input["sensor_data"][0]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "wifi", sensor_data_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)