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First order features standardization WIP

sociality-task
primoz 2022-06-09 13:35:15 +00:00
parent 64e41cfa35
commit f371249b99
5 changed files with 40 additions and 14 deletions
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14
config.yaml
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@ -3,7 +3,7 @@
########################################################################################################################
# See https://www.rapids.science/latest/setup/configuration/#participant-files
PIDS: [p031] #p01, p02, p03]
PIDS: [p03] #p01, p02, p03]
# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
CREATE_PARTICIPANT_FILES:
@ -183,7 +183,7 @@ PHONE_CALLS:
CONTAINER: call
PROVIDERS:
RAPIDS:
COMPUTE: True
COMPUTE: False
FEATURES_TYPE: EPISODES # EVENTS or EPISODES
CALL_TYPES: [missed, incoming, outgoing]
FEATURES:
@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER:
FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
CR:
COMPUTE: True
COMPUTE: False
FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
WINDOWS:
COMPUTE: True
@ -499,7 +499,7 @@ EMPATICA_HEARTRATE:
CONTAINER: HR
PROVIDERS:
DBDP:
COMPUTE: True
COMPUTE: False
FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"]
SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py
@ -512,7 +512,7 @@ EMPATICA_TEMPERATURE:
FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
CR:
COMPUTE: True
COMPUTE: False
FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean",
"stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
WINDOWS:
@ -531,14 +531,14 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
CR:
COMPUTE: True
COMPUTE: False
FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic',
'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore',
'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease',
'significantDecrease']
WINDOWS:
COMPUTE: True
COMPUTE: False
WINDOW_LENGTH: 60 # specify window length in seconds
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
STANDARDIZE_SO_FEATURES: True

1
data/external/timesegments_periodic.csv vendored
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@ -1,3 +1,2 @@
label,start_time,length,repeats_on,repeats_value
daily,00:00:00,23H 59M 59S,every_day,0
E4baseline,01:00:00,3H,every_day,0

1 label start_time length repeats_on repeats_value
2 daily 00:00:00 23H 59M 59S every_day 0
E4baseline 01:00:00 3H every_day 0

10
src/features/empatica_blood_volume_pulse/cr/main.py
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@ -1,5 +1,5 @@
import pandas as pd
from scipy.stats import entropy
from sklearn.preprocessing import StandardScaler
from cr_features.helper_functions import convert_to2d, hrv_features
from cr_features.hrv import extract_hrv_features_2d_wrapper
@ -7,6 +7,8 @@ from cr_features_helper_methods import extract_second_order_features
import sys
# pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', None)
def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
@ -64,8 +66,14 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
requested_window_length, time_segment, filter_data_by_segment)
if calc_windows:
if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
fo_columns = bvp_intraday_features.columns.values[2:]
fo_columns_z_score = [col + "_zscore" for col in fo_columns]
bvp_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(bvp_intraday_features[fo_columns])
so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
bvp_second_order_features = extract_second_order_features(bvp_intraday_features, so_features_names)
return bvp_intraday_features, bvp_second_order_features
return bvp_intraday_features

11
src/features/empatica_inter_beat_interval/cr/main.py
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@ -1,4 +1,5 @@
import pandas as pd
from sklearn.preprocessing import StandardScaler
import numpy as np
from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features
@ -8,8 +9,8 @@ from cr_features_helper_methods import extract_second_order_features
import math
import sys
pd.set_option('display.max_rows', 1000)
#pd.set_option('display.max_columns', None)
# pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', None)
def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
@ -69,8 +70,14 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute,
requested_window_length, time_segment, filter_data_by_segment)
if calc_windows:
if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
fo_columns = ibi_intraday_features.columns.values[2:]
fo_columns_z_score = [col + "_zscore" for col in fo_columns]
ibi_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(ibi_intraday_features[fo_columns])
so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names)
return ibi_intraday_features, ibi_second_order_features

18
src/features/entry.py
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@ -21,12 +21,24 @@ else:
time_segments_file = snakemake.input["time_segments_labels"]
if calc_windows:
window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True)
window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True)
# # Get basic stats from all participant's windows
# fo_means_stds = pd.DataFrame({"mean": window_features.mean(), "median": window_features.median(), "sd": window_features.std(),
# "min": window_features.min(), "max": window_features.max()})
# fo_columns = window_features.columns.values[5:]
# fo_columns_z_score = [col + "_zscore" for col in fo_columns]
# window_features[fo_columns_z_score] = StandardScaler().fit_transform(window_features[fo_columns])
# print(fo_means_stds)
# Z-score SO features by columns
if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
second_order_features[second_order_features.columns[4:]] = StandardScaler().fit_transform(second_order_features[second_order_features.columns[4:]])
# if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
# for indx, fo_mean_std in fo_means_stds.iterrows():
# print(indx, fo_mean_std)
# sys.exit()
window_features.to_csv(snakemake.output[1], index=False)
second_order_features.to_csv(snakemake.output[0], index=False)