First order features standardization WIP
primoz
parent
64e41cfa35
commit
f371249b99
14
config.yaml
14
config.yaml
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@ -3,7 +3,7 @@
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########################################################################################################################
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# See https://www.rapids.science/latest/setup/configuration/#participant-files
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PIDS: [p031] #p01, p02, p03]
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PIDS: [p03] #p01, p02, p03]
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# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
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CREATE_PARTICIPANT_FILES:
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@ -183,7 +183,7 @@ PHONE_CALLS:
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CONTAINER: call
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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COMPUTE: False
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FEATURES_TYPE: EPISODES # EVENTS or EPISODES
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CALL_TYPES: [missed, incoming, outgoing]
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FEATURES:
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@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER:
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FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
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CR:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
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WINDOWS:
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COMPUTE: True
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@ -499,7 +499,7 @@ EMPATICA_HEARTRATE:
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CONTAINER: HR
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"]
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SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py
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@ -512,7 +512,7 @@ EMPATICA_TEMPERATURE:
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FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
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SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
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CR:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean",
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"stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
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WINDOWS:
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@ -531,14 +531,14 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
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FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
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CR:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic',
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'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore',
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'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
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'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease',
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'significantDecrease']
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WINDOWS:
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COMPUTE: True
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COMPUTE: False
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WINDOW_LENGTH: 60 # specify window length in seconds
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
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STANDARDIZE_SO_FEATURES: True
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@ -1,3 +1,2 @@
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label,start_time,length,repeats_on,repeats_value
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daily,00:00:00,23H 59M 59S,every_day,0
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E4baseline,01:00:00,3H,every_day,0
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@ -1,5 +1,5 @@
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import pandas as pd
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from scipy.stats import entropy
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from sklearn.preprocessing import StandardScaler
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from cr_features.helper_functions import convert_to2d, hrv_features
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from cr_features.hrv import extract_hrv_features_2d_wrapper
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@ -7,6 +7,8 @@ from cr_features_helper_methods import extract_second_order_features
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import sys
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# pd.set_option('display.max_rows', 1000)
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pd.set_option('display.max_columns', None)
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def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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@ -64,8 +66,14 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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requested_window_length, time_segment, filter_data_by_segment)
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if calc_windows:
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if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
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fo_columns = bvp_intraday_features.columns.values[2:]
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fo_columns_z_score = [col + "_zscore" for col in fo_columns]
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bvp_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(bvp_intraday_features[fo_columns])
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so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
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bvp_second_order_features = extract_second_order_features(bvp_intraday_features, so_features_names)
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return bvp_intraday_features, bvp_second_order_features
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return bvp_intraday_features
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@ -1,4 +1,5 @@
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import pandas as pd
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from sklearn.preprocessing import StandardScaler
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import numpy as np
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from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features
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@ -8,8 +9,8 @@ from cr_features_helper_methods import extract_second_order_features
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import math
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import sys
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pd.set_option('display.max_rows', 1000)
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#pd.set_option('display.max_columns', None)
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# pd.set_option('display.max_rows', 1000)
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pd.set_option('display.max_columns', None)
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def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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@ -69,8 +70,14 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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if calc_windows:
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if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
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fo_columns = ibi_intraday_features.columns.values[2:]
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fo_columns_z_score = [col + "_zscore" for col in fo_columns]
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ibi_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(ibi_intraday_features[fo_columns])
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so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
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ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names)
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return ibi_intraday_features, ibi_second_order_features
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@ -23,10 +23,22 @@ else:
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if calc_windows:
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window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True)
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# Z-score SO features by columns
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if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
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second_order_features[second_order_features.columns[4:]] = StandardScaler().fit_transform(second_order_features[second_order_features.columns[4:]])
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# # Get basic stats from all participant's windows
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# fo_means_stds = pd.DataFrame({"mean": window_features.mean(), "median": window_features.median(), "sd": window_features.std(),
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# "min": window_features.min(), "max": window_features.max()})
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# fo_columns = window_features.columns.values[5:]
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# fo_columns_z_score = [col + "_zscore" for col in fo_columns]
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# window_features[fo_columns_z_score] = StandardScaler().fit_transform(window_features[fo_columns])
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# print(fo_means_stds)
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# Z-score SO features by columns
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# if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
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# for indx, fo_mean_std in fo_means_stds.iterrows():
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# print(indx, fo_mean_std)
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# sys.exit()
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window_features.to_csv(snakemake.output[1], index=False)
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second_order_features.to_csv(snakemake.output[0], index=False)
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