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Extraction of additional SO features. Min/max has been changed to nsmallest/nlargest means.

sociality-task
primoz 2022-06-10 12:34:48 +00:00
parent f371249b99
commit e1d7607de4
6 changed files with 72 additions and 38 deletions
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44
config.yaml
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@ -484,13 +484,13 @@ EMPATICA_ACCELEROMETER:
FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
CR:
COMPUTE: False
COMPUTE: True
FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
WINDOWS:
COMPUTE: True
WINDOW_LENGTH: 15 # specify window length in seconds
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
STANDARDIZE_SO_FEATURES: True
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
STANDARDIZE_FEATURES: False
SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
@ -512,14 +512,14 @@ EMPATICA_TEMPERATURE:
FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
CR:
COMPUTE: False
COMPUTE: True
FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean",
"stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
WINDOWS:
COMPUTE: True
WINDOW_LENGTH: 300 # specify window length in seconds
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
STANDARDIZE_SO_FEATURES: True
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
STANDARDIZE_FEATURES: False
SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
# See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
@ -531,17 +531,17 @@ 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: False
COMPUTE: True
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: False
COMPUTE: True
WINDOW_LENGTH: 60 # specify window length in seconds
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
STANDARDIZE_SO_FEATURES: True
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', count_windows, eda_num_peaks_non_zero]
STANDARDIZE_FEATURES: False
SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
# See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
@ -559,8 +559,8 @@ EMPATICA_BLOOD_VOLUME_PULSE:
WINDOWS:
COMPUTE: True
WINDOW_LENGTH: 300 # specify window length in seconds
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
STANDARDIZE_SO_FEATURES: True
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
STANDARDIZE_FEATURES: False
SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
# See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
@ -579,8 +579,8 @@ EMPATICA_INTER_BEAT_INTERVAL:
WINDOWS:
COMPUTE: True
WINDOW_LENGTH: 300 # specify window length in seconds
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
STANDARDIZE_SO_FEATURES: True
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
STANDARDIZE_FEATURES: False
SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
# See https://www.rapids.science/latest/features/empatica-tags/
@ -662,4 +662,18 @@ ALL_CLEANING_OVERALL:
COMPUTE: True
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
CORR_THRESHOLD: 0.95
SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
########################################################################################################################
# Z-score standardization #
########################################################################################################################
STANDARDIZATION:
COMPUTE: True
EXCECUTE_FULL_PIPELINE: False # Standardization to be calculated from feature extraction step including merging all sensors and participants steps (in seperate standardization file)
EMPATICA_STANDARDIZATION:
PROVIDERS:
CR:
COMPUTE: False
TYPE: FROM_FIRST_ORDER # FROM_FIRST_ORDER or FROM_SECOND_ORDER(not implemented)
SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R

17
src/features/cr_features_helper_methods.py
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@ -6,16 +6,25 @@ import sys
def extract_second_order_features(intraday_features, so_features_names):
if not intraday_features.empty:
so_features = pd.DataFrame()
#print(intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).nsmallest())
if "mean" in so_features_names:
so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_mean")], axis=1)
if "median" in so_features_names:
so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).median().add_suffix("_SO_median")], axis=1)
if "sd" in so_features_names:
so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).std().add_suffix("_SO_sd")], axis=1)
if "max" in so_features_names:
so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).max().add_suffix("_SO_max")], axis=1)
if "min" in so_features_names:
so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).min().add_suffix("_SO_min")], axis=1)
if "nlargest_mean" in so_features_names: # largest 5 -- maybe there is a faster groupby solution?
for column in intraday_features.columns[2:]:
so_features[column+"_SO_nlargest_mean"] = intraday_features.drop("level_1", axis=1).groupby("local_segment")[column].apply(lambda x: x.nlargest(5).mean())
if "nsmallest_mean" in so_features_names: # smallest 5 -- maybe there is a faster groupby solution?
for column in intraday_features.columns[2:]:
so_features[column+"_SO_nsmallest_mean"] = intraday_features.drop("level_1", axis=1).groupby("local_segment")[column].apply(lambda x: x.nsmallest(5).mean())
if "count_windows" in so_features_names:
so_features["SO_windowsCount"] = intraday_features.groupby(["local_segment"]).count()["level_1"]
# numPeaksNonZero specialized for EDA sensor
if "eda_num_peaks_non_zero" in so_features_names and "numPeaks" in intraday_features.columns:
so_features["SO_numPeaksNonZero"] = intraday_features.groupby("local_segment")["numPeaks"].apply(lambda x: (x!=0).sum())
so_features.reset_index(inplace=True)

2
src/features/empatica_blood_volume_pulse/cr/main.py
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@ -66,7 +66,7 @@ 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):
if provider["WINDOWS"].get("STANDARDIZE_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])

4
src/features/empatica_inter_beat_interval/cr/main.py
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@ -33,7 +33,7 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
signal_2D = \
convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[0],
ibi_timings = \
convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1],
convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1],
sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
else:
ibi_intraday_features = \
@ -70,7 +70,7 @@ 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):
if provider["WINDOWS"].get("STANDARDIZE_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])

16
src/features/entry.py
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@ -22,23 +22,7 @@ else:
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)
# # 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):
# 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)

27
tests/scripts/zero_vals.py 100644
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@ -0,0 +1,27 @@
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants
# path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv"
path = "/rapids/data/interim/p031/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
# path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv"
# path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
# path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv"
df = pd.read_csv(path)
print(df)
is_NaN = df.isnull()
df = df[df["empatica_electrodermal_activity_cr_numPeaks"]]
print(df)
# row_has_NaN = is_NaN. any(axis=1)
# rows_with_NaN = df[row_has_NaN]
# print(rows_with_NaN.size)
# sns.heatmap(df.isna(), cbar=False)
plt.savefig('eda_windows_p03_window_60_thresh_default.png', bbox_inches='tight')