Extraction of additional SO features. Min/max has been changed to nsmallest/nlargest means.
primoz
parent
f371249b99
commit
e1d7607de4
42
config.yaml
42
config.yaml
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@ -484,13 +484,13 @@ 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: False
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COMPUTE: True
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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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WINDOW_LENGTH: 15 # 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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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
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STANDARDIZE_FEATURES: False
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SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
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@ -512,14 +512,14 @@ 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: False
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COMPUTE: True
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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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COMPUTE: True
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WINDOW_LENGTH: 300 # 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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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
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STANDARDIZE_FEATURES: False
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SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
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@ -531,17 +531,17 @@ 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: False
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COMPUTE: True
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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: False
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COMPUTE: True
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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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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', count_windows, eda_num_peaks_non_zero]
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STANDARDIZE_FEATURES: False
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
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@ -559,8 +559,8 @@ EMPATICA_BLOOD_VOLUME_PULSE:
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WINDOWS:
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COMPUTE: True
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WINDOW_LENGTH: 300 # 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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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
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STANDARDIZE_FEATURES: False
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SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
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@ -579,8 +579,8 @@ EMPATICA_INTER_BEAT_INTERVAL:
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WINDOWS:
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COMPUTE: True
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WINDOW_LENGTH: 300 # 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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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows']
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STANDARDIZE_FEATURES: False
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SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-tags/
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@ -663,3 +663,17 @@ ALL_CLEANING_OVERALL:
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MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
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CORR_THRESHOLD: 0.95
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SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
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########################################################################################################################
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# Z-score standardization #
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########################################################################################################################
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STANDARDIZATION:
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COMPUTE: True
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EXCECUTE_FULL_PIPELINE: False # Standardization to be calculated from feature extraction step including merging all sensors and participants steps (in seperate standardization file)
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EMPATICA_STANDARDIZATION:
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PROVIDERS:
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CR:
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COMPUTE: False
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TYPE: FROM_FIRST_ORDER # FROM_FIRST_ORDER or FROM_SECOND_ORDER(not implemented)
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SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
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@ -6,16 +6,25 @@ import sys
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def extract_second_order_features(intraday_features, so_features_names):
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if not intraday_features.empty:
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so_features = pd.DataFrame()
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#print(intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).nsmallest())
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if "mean" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_mean")], axis=1)
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if "median" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).median().add_suffix("_SO_median")], axis=1)
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if "sd" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).std().add_suffix("_SO_sd")], axis=1)
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if "max" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).max().add_suffix("_SO_max")], axis=1)
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if "min" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).min().add_suffix("_SO_min")], axis=1)
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if "nlargest_mean" in so_features_names: # largest 5 -- maybe there is a faster groupby solution?
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for column in intraday_features.columns[2:]:
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so_features[column+"_SO_nlargest_mean"] = intraday_features.drop("level_1", axis=1).groupby("local_segment")[column].apply(lambda x: x.nlargest(5).mean())
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if "nsmallest_mean" in so_features_names: # smallest 5 -- maybe there is a faster groupby solution?
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for column in intraday_features.columns[2:]:
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so_features[column+"_SO_nsmallest_mean"] = intraday_features.drop("level_1", axis=1).groupby("local_segment")[column].apply(lambda x: x.nsmallest(5).mean())
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if "count_windows" in so_features_names:
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so_features["SO_windowsCount"] = intraday_features.groupby(["local_segment"]).count()["level_1"]
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# numPeaksNonZero specialized for EDA sensor
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if "eda_num_peaks_non_zero" in so_features_names and "numPeaks" in intraday_features.columns:
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so_features["SO_numPeaksNonZero"] = intraday_features.groupby("local_segment")["numPeaks"].apply(lambda x: (x!=0).sum())
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so_features.reset_index(inplace=True)
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@ -66,7 +66,7 @@ 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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if provider["WINDOWS"].get("STANDARDIZE_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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@ -70,7 +70,7 @@ 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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if provider["WINDOWS"].get("STANDARDIZE_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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@ -23,22 +23,6 @@ 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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# # 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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@ -0,0 +1,27 @@
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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# path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants
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# path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv"
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path = "/rapids/data/interim/p031/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
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# path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv"
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# path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
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# path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv"
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df = pd.read_csv(path)
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print(df)
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is_NaN = df.isnull()
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df = df[df["empatica_electrodermal_activity_cr_numPeaks"]]
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print(df)
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# row_has_NaN = is_NaN. any(axis=1)
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# rows_with_NaN = df[row_has_NaN]
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# print(rows_with_NaN.size)
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# sns.heatmap(df.isna(), cbar=False)
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plt.savefig('eda_windows_p03_window_60_thresh_default.png', bbox_inches='tight')
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