Refactor screen features: replace "metrics" with "features"
Co-authored-by: Meng Li <AnnieLM1996@gmail.com>pull/95/head
Mingze Cao
parent
8d4a0718e8
commit
e0d19cbc1b
|
|
@ -89,7 +89,7 @@ BATTERY:
|
|||
SCREEN:
|
||||
DAY_SEGMENTS: *day_segments
|
||||
REFERENCE_HOUR_FIRST_USE: 0
|
||||
METRICS_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"]
|
||||
FEATURES_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"]
|
||||
EPISODE_TYPES: ["unlock"]
|
||||
|
||||
LIGHT:
|
||||
|
|
|
|||
|
|
@ -885,7 +885,7 @@ See `Screen Config Code`_
|
|||
- Apply readable dateime to Screen dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
|
||||
- Extract the deltas from the Screen dataset: expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"]),
|
||||
|
||||
- Extract Screen Metrics:
|
||||
- Extract Screen Features:
|
||||
|
||||
| ``expand("data/processed/{pid}/screen_{day_segment}.csv",``
|
||||
| ``pid=config["PIDS"],``
|
||||
|
|
@ -905,9 +905,9 @@ See `Screen Config Code`_
|
|||
|
||||
- **Script:** ``src/features/screen_deltas.R`` - See the screen_deltas.R_ script.
|
||||
|
||||
- **Rule:** ``rules/features.snakefile/screen_metrics`` - See the screen_metrics_ rule.
|
||||
- **Rule:** ``rules/features.snakefile/screen_features`` - See the screen_features_ rule.
|
||||
|
||||
- **Script:** ``src/features/screen_metrics.py`` - See the screen_metrics.py_ script.
|
||||
- **Script:** ``src/features/screen_features.py`` - See the screen_features.py_ script.
|
||||
|
||||
.. _screen-parameters:
|
||||
|
||||
|
|
@ -917,16 +917,16 @@ See `Screen Config Code`_
|
|||
Name Description
|
||||
=============== ===================
|
||||
day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
|
||||
metrics_events The different measures that can be retrieved from the events in the Screen dataset. See :ref:`Available Screen Events Metrics <screen-events-available-metrics>` Table below
|
||||
metrics_deltas The different measures that can be retrieved from the episodes extracted from the Screen dataset. See :ref:`Available Screen Episodes Metrics <screen-episodes-available-metrics>` Table below
|
||||
features_events The different measures that can be retrieved from the events in the Screen dataset. See :ref:`Available Screen Events Features <screen-events-available-features>` Table below
|
||||
features_deltas The different measures that can be retrieved from the episodes extracted from the Screen dataset. See :ref:`Available Screen Episodes Features <screen-episodes-available-features>` Table below
|
||||
episodes The action that defines an episode
|
||||
=============== ===================
|
||||
|
||||
.. _screen-events-available-metrics:
|
||||
.. _screen-events-available-features:
|
||||
|
||||
..
|
||||
**Available Screen Events Metrics**
|
||||
The following table shows a list of the available metrics for Screen Events.
|
||||
**Available Screen Events Features**
|
||||
The following table shows a list of the available features for Screen Events.
|
||||
================= ============== =============
|
||||
Name Units Description
|
||||
================= ============== =============
|
||||
|
|
@ -935,11 +935,11 @@ episodes The action that defines an episode
|
|||
unlocksperminute Unlock events Unlock events per minute: The average of the number of unlock events that occur in a minute
|
||||
================= ============== =============
|
||||
|
||||
.. _screen-episodes-available-metrics:
|
||||
.. _screen-episodes-available-features:
|
||||
|
||||
**Available Screen Episodes Metrics**
|
||||
**Available Screen Episodes Features**
|
||||
|
||||
The following table shows a list of the available metrics for Screen Episodes.
|
||||
The following table shows a list of the available features for Screen Episodes.
|
||||
|
||||
============= ========= =============
|
||||
Name Units Description
|
||||
|
|
@ -1187,8 +1187,8 @@ stddurationactivebout minutes Std duration active bout: The standard
|
|||
.. _`Screen Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L88
|
||||
.. _screen_deltas: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L33
|
||||
.. _screen_deltas.R: https://github.com/carissalow/rapids/blob/master/src/features/screen_deltas.R
|
||||
.. _screen_metrics: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L97
|
||||
.. _screen_metrics.py: https://github.com/carissalow/rapids/blob/master/src/features/screen_metrics.py
|
||||
.. _screen_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L97
|
||||
.. _screen_features.py: https://github.com/carissalow/rapids/blob/master/src/features/screen_features.py
|
||||
.. _`Fitbit: Heart Rate Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L113
|
||||
.. _fitbit_with_datetime: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/preprocessing.snakefile#L94
|
||||
.. _fitbit_readable_datetime.py: https://github.com/carissalow/rapids/blob/master/src/data/fitbit_readable_datetime.py
|
||||
|
|
|
|||
|
|
@ -96,20 +96,20 @@ rule battery_metrics:
|
|||
script:
|
||||
"../src/features/battery_metrics.py"
|
||||
|
||||
rule screen_metrics:
|
||||
rule screen_features:
|
||||
input:
|
||||
screen_deltas = "data/processed/{pid}/screen_deltas.csv",
|
||||
phone_sensed_bins = "data/interim/{pid}/phone_sensed_bins.csv"
|
||||
params:
|
||||
day_segment = "{day_segment}",
|
||||
reference_hour_first_use = config["SCREEN"]["REFERENCE_HOUR_FIRST_USE"],
|
||||
metrics_deltas = config["SCREEN"]["METRICS_DELTAS"],
|
||||
features_deltas = config["SCREEN"]["FEATURES_DELTAS"],
|
||||
episode_types = config["SCREEN"]["EPISODE_TYPES"],
|
||||
bin_size = config["PHONE_VALID_SENSED_DAYS"]["BIN_SIZE"]
|
||||
output:
|
||||
"data/processed/{pid}/screen_{day_segment}.csv"
|
||||
script:
|
||||
"../src/features/screen_metrics.py"
|
||||
"../src/features/screen_features.py"
|
||||
|
||||
rule light_metrics:
|
||||
input:
|
||||
|
|
|
|||
|
|
@ -5,27 +5,27 @@ import itertools
|
|||
from datetime import datetime, timedelta, time
|
||||
from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
|
||||
|
||||
def getEpisodeDurationFeatures(screen_deltas, episode, metrics, phone_sensed_bins, bin_size, reference_hour_first_use):
|
||||
def getEpisodeDurationFeatures(screen_deltas, episode, features, phone_sensed_bins, bin_size, reference_hour_first_use):
|
||||
screen_deltas_episode = screen_deltas[screen_deltas["episode"] == episode]
|
||||
duration_helper = pd.DataFrame()
|
||||
if "countepisode" in metrics:
|
||||
if "countepisode" in features:
|
||||
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).count()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_countepisode" + episode})], axis = 1)
|
||||
if "episodepersensedminutes" in metrics:
|
||||
if "episodepersensedminutes" in features:
|
||||
for date, row in screen_deltas_episode.groupby(["local_start_date"]).count()[["time_diff"]].iterrows():
|
||||
sensed_minutes = phone_sensed_bins.loc[date, :].sum() * bin_size
|
||||
episode_per_sensedminutes = row["time_diff"] / (1 if sensed_minutes == 0 else sensed_minutes)
|
||||
duration_helper.loc[date, "screen_" + day_segment + "_episodepersensedminutes" + episode] = episode_per_sensedminutes
|
||||
if "sumduration" in metrics:
|
||||
if "sumduration" in features:
|
||||
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).sum()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_sumduration" + episode})], axis = 1)
|
||||
if "maxduration" in metrics:
|
||||
if "maxduration" in features:
|
||||
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).max()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_maxduration" + episode})], axis = 1)
|
||||
if "minduration" in metrics:
|
||||
if "minduration" in features:
|
||||
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).min()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_minduration" + episode})], axis = 1)
|
||||
if "avgduration" in metrics:
|
||||
if "avgduration" in features:
|
||||
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).mean()[["time_diff"]].rename(columns = {"time_diff":"screen_" + day_segment + "_avgduration" + episode})], axis = 1)
|
||||
if "stdduration" in metrics:
|
||||
if "stdduration" in features:
|
||||
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).std()[["time_diff"]].rename(columns = {"time_diff":"screen_" + day_segment + "_stdduration" + episode})], axis = 1)
|
||||
if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in metrics:
|
||||
if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features:
|
||||
duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_deltas_episode.groupby(["local_start_date"]).first()[["local_start_date_time"]].local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 3600 + x.to_pydatetime().minute * 60 + x.to_pydatetime().second)).rename(columns = {"local_start_date_time":"screen_" + day_segment + "_firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1)
|
||||
return duration_helper
|
||||
|
||||
|
|
@ -36,15 +36,15 @@ phone_sensed_bins[phone_sensed_bins > 0] = 1
|
|||
|
||||
day_segment = snakemake.params["day_segment"]
|
||||
reference_hour_first_use = snakemake.params["reference_hour_first_use"]
|
||||
metrics_deltas = snakemake.params["metrics_deltas"]
|
||||
features_deltas = snakemake.params["features_deltas"]
|
||||
episode_types = snakemake.params["episode_types"]
|
||||
bin_size = snakemake.params["bin_size"]
|
||||
|
||||
metrics_deltas = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in metrics_deltas]
|
||||
features_deltas = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_deltas]
|
||||
|
||||
metrics_deltas_name = ["".join(metric) for metric in itertools.product(metrics_deltas, episode_types)]
|
||||
features_deltas_name = ["".join(feature) for feature in itertools.product(features_deltas, episode_types)]
|
||||
|
||||
screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in metrics_deltas_name])
|
||||
screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in features_deltas_name])
|
||||
if not screen_deltas.empty:
|
||||
# preprocess day_segment and episodes
|
||||
screen_deltas = splitOvernightEpisodes(screen_deltas, [], ["episode"])
|
||||
|
|
@ -55,7 +55,7 @@ if not screen_deltas.empty:
|
|||
if not screen_deltas.empty:
|
||||
screen_features = pd.DataFrame()
|
||||
for episode in episode_types:
|
||||
screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_deltas, episode, metrics_deltas, phone_sensed_bins, bin_size, reference_hour_first_use)], axis=1)
|
||||
screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_deltas, episode, features_deltas, phone_sensed_bins, bin_size, reference_hour_first_use)], axis=1)
|
||||
|
||||
if not screen_features.empty:
|
||||
screen_features = screen_features.rename_axis("local_date").reset_index()
|
||||
Loading…
Reference in New Issue