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Added cf provider for EDA feature processing.

sociality-task
Primoz 2022-03-23 15:13:53 +00:00
parent d3a3f01f29
commit 191e53e543
4 changed files with 22 additions and 43 deletions
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1
.gitignore vendored
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@ -93,6 +93,7 @@ packrat/*
# exclude data from source control by default
data/external/*
!/data/external/empatica/empatica1/E4 Data.zip
!/data/external/.gitkeep
!/data/external/stachl_application_genre_catalogue.csv
!/data/external/timesegments*.csv

9
config.yaml
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@ -509,7 +509,14 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
CF:
COMPUTE: True
FEATURES: ['mean', 'std', 'q25', 'q75', 'qd'] # To-Do add remaining features from CF helper file.
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', 'maxPeakResponseSlopeBefore',
'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate',
'significantIncrease', 'significantDecrease']
SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py
# See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/

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data/external/empatica/empatica1/E4 Data.zip vendored
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55
src/features/empatica_electrodermal_activity/cf/main.py
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@ -1,57 +1,29 @@
import pandas as pd
from scipy.stats import entropy
import sys
sys.path.insert(1, '/workspaces/rapids/calculatingfeatures')
from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames
from CalculatingFeatures.calculate_features import calculateFeatures
def statsFeatures(eda_data, features, eda_features):
col_name = "electrodermal_activity"
if "sumeda" in features:
eda_features["sumeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].sum()
if "maxeda" in features:
eda_features["maxeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].max()
if "mineda" in features:
eda_features["mineda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].min()
if "avgeda" in features:
eda_features["avgeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].mean()
if "medianeda" in features:
eda_features["medianeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].median()
if "modeeda" in features:
eda_features["modeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(lambda x: pd.Series.mode(x)[0])
if "stdeda" in features:
eda_features["stdeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].std()
if "diffmaxmodeeda" in features:
eda_features["diffmaxmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].max() - \
eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(lambda x: pd.Series.mode(x)[0])
if "diffminmodeeda" in features:
eda_features["diffminmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(lambda x: pd.Series.mode(x)[0]) - \
eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].min()
if "entropyeda" in features:
eda_features["entropyeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(entropy)
pd.set_option('display.max_columns', None)
return eda_features
def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment):
eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
if not eda_intraday_data.empty:
if not eda_intraday_data.empty:
eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
if not eda_intraday_data.empty:
eda_intraday_features = pd.DataFrame()
# get stats of eda
eda_intraday_features = statsFeatures(eda_intraday_data, features, eda_intraday_features)
# apply a method from calculate features module
eda_intraday_features = \
eda_intraday_data.groupby('local_segment').apply(\
lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=4, featureNames=features))
eda_intraday_features.reset_index(inplace=True)
@ -63,8 +35,7 @@ def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
requested_intraday_features = provider["FEATURES"]
# name of the features this function can compute
base_intraday_features_names = ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda",
"diffminmodeeda", "entropyeda"]
base_intraday_features_names = gsrFeatureNames
# the subset of requested features this function can compute
intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))