import glob
from os import makedirs, mkdir
from os.path import join, exists
import pandas as pd
import numpy as np
import json
import ipdb
import re
import pickle
import sys
import time
from zipfile import ZipFile
from joblib import dump, load
import argparse
from datetime import datetime, timedelta, timezone, timedelta
import pytz
import matplotlib.pyplot as plt
from functools import partial
from collections import Counter
from itertools import repeat, chain, combinations
from multiprocessing import Pool, cpu_count
import tensorflow as tf
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
from sklearn.preprocessing import PolynomialFeatures, LabelEncoder
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import accuracy_score
from tsfresh.feature_extraction import extract_features
from tsfresh.feature_extraction import settings as tsfresh_settings
from tsfresh.feature_selection import relevance
from tsfresh.utilities.string_manipulation import get_config_from_string
from modules.datapipeline import get_file_list, load_and_snip, load_data, \
load_split_data, load_harness_data
from modules.digitalsignalprocessing import vectorized_slide_win as vsw
from modules.digitalsignalprocessing import imu_signal_processing
from modules.digitalsignalprocessing import bvp_signal_processing
from modules.digitalsignalprocessing import hernandez_sp, reject_artefact
from modules.digitalsignalprocessing import do_pad_fft,\
pressure_signal_processing, infer_frequency, movingaverage
from modules.utils import *
from modules.evaluations import Evaluation
from modules.datapipeline import get_windowed_data, DataSynchronizer,\
parallelize_dataframe
from modules.datapipeline import ProjectFileHandler
from models.ardregression import ARDRegressionClass
from models.knn import KNNClass
from models.svm import SVMClass
from models.lda import LDAClass
from models.svr import SVRClass
from models.logisticregression import LogisticRegressionClass
from models.linearregression import LinearRegressionClass
from models.neuralnet import FNN_HyperModel, LSTM_HyperModel, TunerClass,\
CNN1D_HyperModel
from models.ridgeclass import RidgeClass
from models.resnet import Regressor_RESNET, Classifier_RESNET
from models.xgboostclass import XGBoostClass
from pprint import PrettyPrinter
from sktime.transformations.panel.rocket import (
MiniRocket,
MiniRocketMultivariate,
MiniRocketMultivariateVariable,
)
from config import WINDOW_SIZE, WINDOW_SHIFT, IMU_FS, DATA_DIR, BR_FS\
, FS_RESAMPLE, PPG_FS
N_SUBJECT_MAX = 6
IMU_COLS = ['acc_x', 'acc_y', 'acc_z', 'gyro_x', 'gyro_y', 'gyro_z']
def utc_to_local(utc_dt, tz=None):
"""Converts UTC datetime to specified timezone
Arguments
---------
utc_dt : datetime
input datetime to convert
tz : pytz.timezone
timezone
Returns
-------
datetime
"""
return utc_dt.replace(tzinfo=timezone.utc).astimezone(tz=tz)
def datetime_from_utc_to_local(utc_datetime):
"""Converts UTC datetime to local time
Arguments
---------
utc_dt : datetime
input datetime to convert
Returns
-------
datetime
"""
now_timestamp = time.time()
offset = datetime.fromtimestamp(now_timestamp) - datetime.utcfromtimestamp(now_timestamp)
return utc_datetime + offset
# Load data
def load_bioharness_file(f:str, skiprows=0, skipfooter=0, **kwargs):
"""
Load and retrieve bioharness file. Interpolates any empty time rows
Arguments
---------
f : str
filename
skiprows : int
num. of rows to skip from top
skipfooter : int
num. of rows to skip from bottom
**kwargs
Returns
-------
pandas.DataFrame
"""
df_list = []
fmt = "%d/%m/%Y %H:%M:%S.%f"
# Set keyword arguments for read_csv
method = partial(pd.read_csv, skipinitialspace=True,
skiprows=list(range(1, skiprows+1)),
skipfooter=skipfooter,
header=0,
**kwargs
)
df = method(f)
if 'Time' not in df.columns.values:
# Set to datetime format
df['Time'] = pd.to_datetime(
df.rename(columns={'Date':'Day'})[
['Day','Month','Year']]) \
+ pd.to_timedelta(df['ms'], unit='ms')
# Interpolate empty time rows
if pd.isna(df['Time']).any():
df['Time'].interpolate(inplace=True)
df['Time'] = pd.to_datetime(df['Time'], format=fmt)
df['Time'] = df['Time'].dt.strftime(fmt)
return df
def load_bioharness_files(f_list:list, skiprows=0, skipfooter=0, **kwargs):
"""
Appends the output for load_bioharness_file
Arguments
---------
f_list : list
list of bioharness files to read
skiprows : int
num. of rows to skip from top
skipfooter : int
num. of rows to skip from bottom
**kwargs
Returns
-------
pandas.DataFrame
"""
df_list = []
for f in f_list:
df_list.append(load_bioharness_file(f))
df = pd.concat(df_list, ignore_index=True)
return df
def bioharness_datetime_to_seconds(val):
"""
Converts the bioharness datetime to seconds
Arguments
---------
val : str
bioharness time string
Returns
-------
float
"""
fmt = "%d/%m/%Y %H:%M:%S.%f"
dstr = datetime.strptime(val, fmt)
seconds = dstr.timestamp()
return seconds
def load_imu_file(imu_file:str):
"""
Load and retrieve the specified tobtii imu compressed file
Arguments
---------
imu_file : str
Tobii Glasses IMU file to read in gzip compressed format
Returns
-------
pd.DataFrame, dict
"""
hdr_file = imu_file.replace('imudata.gz', 'recording.g3')
df = pd.read_json(imu_file, lines=True, compression='gzip')
hdr = pd.read_json(hdr_file, orient='index')
hdr = hdr.to_dict().pop(0)
if df.empty: return df, hdr
# Create DataFrame from data column
data_df = pd.DataFrame(df['data'].tolist())
df = pd.concat([df.drop('data', axis=1), data_df], axis=1)
iso_tz = hdr['created']
tzinfo = pytz.timezone(hdr['timezone'])
# adjust for UTC
start_time = datetime.fromisoformat(iso_tz[:-1])
start_time = utc_to_local(start_time, tz=tzinfo).astimezone(tzinfo)
# Drop NA rows
na_inds = df.loc[pd.isna(df['accelerometer']), :].index.values
df.drop(index=na_inds, inplace=True)
# Interpolate times to account for any empty rows
imu_times = df['timestamp'].values
df['timestamp_interp'] = imu_times
df['timestamp_interp'] = df['timestamp_interp'].interpolate()
imu_times = df['timestamp_interp'].values
# Convert to local time
imu_datetimes = [start_time + timedelta(seconds=val) \
for val in imu_times]
imu_s = np.array([time.timestamp() for time in imu_datetimes])
df['sec'] = imu_s
# Remove any rows that are beyond 3-hours, accommodating for erroneous data
time_check_thold = df['sec'].min() + 3*3600
mask = df['sec'] > time_check_thold
if np.any(mask):
df = df[np.logical_not(mask)]
return df, hdr
def load_imu_files(f_list:list):
"""
Appends the output for load_imu_file
Arguments
---------
f_list : list
list of bioharness files to read
Returns
-------
pandas.DataFrame, list
"""
data, hdr = [], []
tmp = []
with Pool(int(cpu_count()//1.5)) as p:
tmp = p.map(load_imu_file, f_list)
data, hdr = zip(*tmp)
# for f in f_list:
# tmp.append(load_imu_file(f))
# for l in tmp:
# data.append(l[0])
# hdr.append(l[1])
data_df = pd.concat(data, axis=0)
data_df.sort_values(by='sec', inplace=True)
return data_df, hdr
def load_e4_file(e4_file:str):
"""Loads BVP data from the specified zip compressed e4 file and the start
time and sampling frequency as a dict.
Attributes
----------
e4_file : str
.zip e4 filename to load
Returns
-------
pandas.DataFrame, dict
"""
zip_file = ZipFile(e4_file)
dfs = {csv_file.filename: pd.read_csv(zip_file.open(csv_file.filename)
,header=None)
for csv_file in zip_file.infolist()
if csv_file.filename.endswith('.csv') and csv_file.file_size>0}
bvp = dfs["BVP.csv"]
# First row is the initial time of the session as unix time.
# Second row is the sample rate in Hz
t0 = bvp.iloc[0].values[0]
fs = bvp.iloc[1].values[0]
nsamples = len(bvp) - 2
# t0_datetime = datetime.utcfromtimestamp(t0)
# t0_local = datetime_from_utc_to_local(t0_datetime)
t0_datetime = datetime.fromtimestamp(t0, tz=timezone.utc)
t0_local = t0_datetime.astimezone(pytz.timezone('Australia/Sydney'))
time = [t0_local.timestamp() + ind*(1/fs) for ind in
range(nsamples)]
tmp = [np.nan, np.nan]
time = tmp + time
bvp.rename(columns={0: "bvp"}, inplace=True)
bvp['sec'] = np.array(time)
head = bvp.iloc[[0, 1]]
bvp.drop(inplace=True, index=[0, 1])
hdr = {'start_time': head.iloc[0,0],
'fs': head.iloc[0,1]}
return bvp, hdr
def load_e4_files(f_list:list):
"""
Appends the output for load_e4_file
Arguments
---------
f_list : list
list of e4 files to read
Returns
-------
pandas.DataFrame, list
"""
tmp = []
data = []
hdr = []
for f in f_list:
tmp.append(load_e4_file(f))
for d, h in tmp:
data.append(d)
hdr.append(h)
data_df = pd.concat(data, axis=0).sort_values(by='sec')
return data_df, hdr
# Synchronising data
def sync_to_ref(df0, df1):
"""
Synchronises both DataFrames
Arguments
---------
df0 : pandas.DataFrame
data to sync
df1 : pandas.DataFrame
data to sync
Returns
-------
pandas.DataFrame, pandas.DataFrame
"""
dsync0 = DataSynchronizer()
dsync1 = DataSynchronizer()
time0 = df0['sec'].values
time1 = df1['sec'].values
t0 = max((time0[0], time1[0]))
t1 = min((time0[-1], time1[-1]))
dsync0.set_bounds(time0, t0, t1)
dsync1.set_bounds(time1, t0, t1)
return dsync0.sync_df(df0), dsync1.sync_df(df1)
# Task for windowing dataframe
def df_win_task(w_inds, df, i, cols):
"""
Performs signal processing on IMU. If BVP values exist in the column
namespace, BVP signal processing is performed. Extract median BR from the
summary bioharness file and max frequency of the PSS wave.
Add index value for each window for tsfresh processing.
Attributes
----------
w_inds : numpy.ndarray
specifies the window indexes for the df
df : pandas.DataFrame
DataFrame to extract window from
i : int
window index
cols : list
columns to perform data processing functions across
Returns
-------
pandas.DataFrame, pandas.DataFrame
"""
time = df['sec'].values
# if w_inds[-1] == 0: return
w_df = df.iloc[w_inds]
t0, t1 = time[w_inds][0], time[w_inds][-1]
diff = time[w_inds[1:]] - time[w_inds[0:-1]]
fs_est = 1/np.median(diff)
if fs_est > 70 and 'acc_x' in cols: fs = IMU_FS
elif fs_est < 70 and 'bvp' in cols: fs = PPG_FS
# Reject window if there is a time difference between rows greater than 20s
mask = np.abs(diff)>20
diff_chk = np.any(mask)
if diff_chk:
return
filt_out = []
for col in cols:
data = w_df[col].values
# DSP
if sum(np.abs(data)) > 0 and np.std(data) > 0:
sd_data = (data - np.mean(data, axis=0))/np.std(data, axis=0)
else:
sd_data = data.copy()
# ys = cubic_interp(sd_data, BR_FS, FS_RESAMPLE)
if col != 'bvp':
filt_out.append(imu_signal_processing(sd_data, fs))
else:
bvp_filt = bvp_signal_processing(sd_data, fs)
filt_out.append(bvp_filt)
x_out = pd.DataFrame(np.array(filt_out).T, columns=cols)
sm_out = w_df['BR'].values
ps_out = w_df['PSS'].values
x_vec_time = np.median(time[w_inds])
# fs = 1/np.mean(diff)
ps_out = pressure_signal_processing(ps_out, fs=fs)
ps_freq = get_max_frequency(ps_out, fs=fs)
y_tmp = np.array([x_vec_time, np.nanmedian(sm_out), ps_freq])
x_out['sec'] = x_vec_time
x_out['id'] = i
y_out = pd.DataFrame([y_tmp], columns=['sec', 'br', 'pss'])
if 'cpm' in w_df.columns.tolist():
cpm_out = int(np.median(w_df['cpm'].values))
y_out['cpm'] = cpm_out
if 'bvp' in cols:
xf, yf = do_pad_fft(bvp_filt, fs=fs)
bv_freq = int(xf[yf.argmax()]*60)
# Uncomment if you wish to extract BVP estimated HR.
# y_out['hr_est'] = bv_freq
return x_out, y_out
def get_max_frequency(data, fs=IMU_FS, fr=0.02):
"""
Returns the maximum frequency after padded fft
Attributes
----------
data : numpy.ndarray
signal to extract max frequency
fs : int
signal sampling frequency (default = IMU_FS)
fr : float
frequency resolution to set pad length (default = 0.02)
Returns
-------
float
"""
xf, yf = do_pad_fft(data, fs=fs, fr=fr)
max_freq = xf[yf.argmax()]*60
return max_freq
def convert_to_float(df):
"""
Converts 'sec', 'pss', 'br', and 'subject' columns to float
Attributes
----------
df : pandas.DataFrame
"""
cols = df.columns.values
if 'sec' in cols:
df['sec'] = df['sec'].astype(float)
if 'pss' in cols:
df['pss'] = df['pss'].astype(float)
if 'br' in cols:
df['br'] = df['br'].astype(float)
if 'subject' in cols:
df['subject'] = df['subject'].astype(float)
def load_and_sync_xsens(subject, sens_list:list=['imu', 'bvp']):
"""
Loads requested sensors from the subject folder and synchronises each to
the beginning and end timestamps. Linearly interpolates the data and
timestamps to match the higher frequency data.
Arguments
---------
subject : str
subject to extract data from (i.e. 'Pilot02', 'S02')
sens_list : list
a list that contains either or both 'imu' and 'bvp'
Returns
-------
pd.DataFrame
"""
assert 'imu' in sens_list or 'bvp' in sens_list, \
f"{sens_list} is not supported, must contain"\
"'imu', 'bvp' or 'imu, bvp'"
pss_df, br_df, imu_df, bvp_df = None, None, None, None
acc_data, gyr_data, bvp_data = None, None, None
# load imu
if 'imu' in sens_list:
imu_list = get_file_list('imudata.gz', sbj=subject)
imu_df_all, imu_hdr_df_all = load_imu_files(imu_list)
# load bioharness
pss_list = get_file_list('*Breathing.csv', sbj=subject)
if len(pss_list) == 0:
pss_list = get_file_list('BR*.csv', sbj=subject)
br_list = get_file_list('*Summary*.csv', sbj=subject)
# load e4 wristband
if 'bvp' in sens_list:
e4_list = get_file_list('*.zip', sbj=subject)
bvp_df_all, bvp_hdr = load_e4_files(e4_list)
bvp_fs = bvp_hdr[0]['fs']
xsens_list = []
# skip the first and last x minute(s)
minutes_to_skip = .5
br_skiprows = br_skipfooter = int(minutes_to_skip*60)
pss_skiprows = pss_skipfooter = int(minutes_to_skip*60*BR_FS)
# load each bioharness file and sync the imu to it
for pss_file, br_file in zip(pss_list, br_list):
xsens_data = {}
pss_df = load_bioharness_file(pss_file, skiprows=pss_skiprows,
skipfooter=pss_skipfooter,
engine='python')
pss_time = pss_df['Time'].map(bioharness_datetime_to_seconds).values\
.reshape(-1, 1)
pss_df['sec'] = pss_time
br_df = load_bioharness_file(br_file, skiprows=br_skiprows,
skipfooter=br_skipfooter,
engine='python')
br_time = br_df['Time'].map(bioharness_datetime_to_seconds).values\
.reshape(-1, 1)
br_df['sec'] = br_time
# sync
if 'imu' in sens_list and 'bvp' in sens_list:
br_df, imu_df = sync_to_ref(br_df, imu_df_all.copy())
pss_df, _ = sync_to_ref(pss_df, imu_df_all.copy())
bvp_df, _ = sync_to_ref(bvp_df_all.copy(), imu_df_all.copy())
elif 'imu' in sens_list and not 'bvp' in sens_list:
br_df, imu_df = sync_to_ref(br_df, imu_df_all.copy())
pss_df, _ = sync_to_ref(pss_df, imu_df_all.copy())
elif not 'imu' in sens_list and 'bvp' in sens_list:
br_df, bvp_df = sync_to_ref(br_df, bvp_df_all.copy())
pss_df, _ = sync_to_ref(pss_df, bvp_df_all.copy())
# extract relevant data
if 'imu' in sens_list:
axes = ['x', 'y', 'z']
acc_data = np.stack(imu_df['accelerometer'].values)
gyr_data = np.stack(imu_df['gyroscope'].values)
x_time = imu_df['sec'].values.reshape(-1, 1)
if 'bvp' in sens_list and 'imu' in sens_list:
bvp_data = bvp_df['bvp'].values
bvp_data = np.interp(x_time, bvp_df['sec'].values, bvp_data)\
.reshape(-1, 1)
elif 'bvp' in sens_list and not 'imu' in sens_list:
bvp_data = bvp_df['bvp'].values
x_time = bvp_df['sec'].values
xsens_data['sec'] = x_time.flatten()
br_col = [col for col in pss_df.columns.values if\
'breathing' in col.lower()][0]
pss_data = pss_df[br_col].values
pss_data = np.interp(x_time, pss_df['sec'].values, pss_data)\
.reshape(-1, 1)
xsens_data['PSS'] = pss_data.flatten()
br_lbl = [col for col in br_df.columns.values if\
'br' in col.lower()][0]
br_data = br_df['BR'].values
br_data = np.interp(x_time, br_df['sec'].values, br_data)\
.reshape(-1, 1)
xsens_data['BR'] = br_data.flatten()
if 'imu' in sens_list:
for i, axis in enumerate(axes):
xsens_data['acc_'+axis] = acc_data.T[i].flatten()
xsens_data['gyro_'+axis] = gyr_data.T[i].flatten()
if 'bvp' in sens_list:
xsens_data['bvp'] = bvp_data.flatten()
xsens_df_tmp = pd.DataFrame(xsens_data)
xsens_list.append(xsens_df_tmp)
if len(xsens_list) > 1:
xsens_df = pd.concat(xsens_list, axis=0, ignore_index=True)
xsens_df.sort_values(by='sec', inplace=True)
xsens_df.reset_index(drop=True, inplace=True)
else:
xsens_df = xsens_list[0]
return xsens_df
def load_tsfresh(xsens_df, home_dir,
window_size=12, window_shift=0.2, fs=IMU_FS,
overwrite=False, data_cols=None, prefix=None, **kwargs):
"""
Loads the tsfresh pickle file, or generates if it does not exist for the
given configuration
Arguments
---------
xsens_df : pandas.DataFrame
synchronised and frequency matched DataFrame with all data and labels
Returns
-------
pd.DataFrame
"""
assert data_cols is not None, "invalid selection for data columns"
# raise NotImplementedError("To be implemented")
if prefix is not None:
pkl_fname = f'{prefix}__winsize_{window_size}__winshift_{window_shift}__tsfresh.pkl'
else:
pkl_fname = f'winsize_{window_size}__winshift_{window_shift}__tsfresh.pkl'
pkl_dir = join(home_dir,
f'tsfresh__winsize_{window_size}__winshift_{window_shift}')
pkl_file = join(pkl_dir, pkl_fname)
if not exists(pkl_dir): mkdir(pkl_dir)
if exists(pkl_file) and not overwrite:
return pd.read_pickle(pkl_file)
assert 'acc_x' in xsens_df.columns.tolist() and \
'gyro_x' in xsens_df.columns.tolist() and \
'bvp' in xsens_df.columns.tolist(), \
"First instance must include the full required dataset. Must have both "\
"IMU and BVP"
x_df, y_df = get_df_windows(xsens_df,
df_win_task,
window_size=window_size,
window_shift=window_shift,
fs=fs,
cols=data_cols,
)
x_features_df = extract_features(
x_df, column_sort='sec',
column_id='id',
# default_fc_parameters=tsfresh_settings.MinimalFCParameters(),
**kwargs
)
x_features_df.fillna(0, inplace=True)
x_features_df.reset_index(drop=True, inplace=True)
x_features_df = x_features_df.reindex(sorted(x_features_df.columns.values),
axis=1)
cols = x_features_df.columns.values
df_out = pd.concat([y_df, x_features_df], axis=1)
df_out.to_pickle(pkl_file)
return df_out
def get_activity_log(subject):
"""
Loads and retrieves the sit and stand file
Arguments
---------
subject: str
subject to retrieve (i.e. 'Pilot02', 'S02')
Returns
-------
pd.DataFrame
"""
activity_list = get_file_list('activity*.csv', sbj=subject)
activity_dfs = [pd.read_csv(f) for f in activity_list]
return pd.concat(activity_dfs, axis=0)
def get_respiration_log(subject):
"""
Loads and retrieves the respiration calibration events, timestamps,
inhale/exhale
Arguments
---------
subject: str
subject to retrieve (i.e. 'Pilot02', 'S02')
Returns
-------
pd.DataFrame
"""
log_list = get_file_list('*.json', sbj=subject)
log_dfs = [pd.read_json(f, convert_dates=False) for f in log_list]
return pd.concat(log_dfs, axis=0)
def get_cal_data(event_df, xsens_df):
"""
Loads and retrieves the respiration calibration data
Arguments
---------
event_df : pandas.DataFrame
timestamp, inhalation, exhalation, and event data from calibration
process
xsens_df : pandas.DataFrame
synchronised and frequency matched DataFrame with all data and labels
Returns
-------
pd.DataFrame
"""
fmt ="%d/%m/%Y %I:%M:%S %p"
cal_list = []
cpms = []
start_sec = 0
stop_sec = 0
for index, row in event_df.iterrows():
event = row['eventTag']
timestamp = row['timestamp']
inhalePeriod = row['inhalePeriod']
exhalePeriod = row['exhalePeriod']
cpm = np.round( 60/(inhalePeriod + exhalePeriod) )
sec = datetime.strptime(str(timestamp), fmt).timestamp()
if event == 'Start':
start_sec = sec
continue
elif event == 'Stop':
stop_sec = min(sec, xsens_df['sec'].values[-1])
dsync = DataSynchronizer()
dsync.set_bounds(xsens_df['sec'].values, start_sec, stop_sec)
sync_df = dsync.sync_df(xsens_df.copy())
cal_data = {'cpm': cpm, 'data': sync_df}
cal_list.append(cal_data)
assert np.round(sync_df.sec.iloc[0])==np.round(start_sec), \
"error with start sync"
assert np.round(sync_df.sec.iloc[-1])==np.round(stop_sec), \
"error with stop sync"
return pd.DataFrame(cal_list)
def get_test_data(cal_df, activity_df, xsens_df, test_standing):
"""
Loads and retrieves the activity timestamps from sitting and standing
events
Arguments
---------
cal_df : pandas.DataFrame
synchronised and frequency matched respiration calibration data
activity_df : pandas.DataFrame
timestamps of activity events
xsens_df : pandas.DataFrame
synchronised and frequency matched DataFrame with all data and labels
test_standing : bool
list of column str
Returns
-------
pd.DataFrame
"""
fmt = "%d/%m/%Y %H:%M:%S"
start_time = cal_df.iloc[-1]['data'].sec.values[-1]
data_df = xsens_df[xsens_df.sec > start_time]
activity_start = 0
activity_end = 0
activity_list = []
for index, row in activity_df.iterrows():
sec = datetime.strptime(row['Timestamps'], fmt).timestamp()
if not test_standing and row['Activity'] == 'standing':
continue
if row['Event'] == 'start':
activity_start = sec
elif row['Event'] == 'end':
activity_stop = sec
dsync = DataSynchronizer()
dsync.set_bounds(data_df['sec'].values, activity_start,
activity_stop)
sync_df = dsync.sync_df(data_df.copy())
activity_data = {'activity': row['Activity'], 'data': sync_df}
activity_list.append(activity_data)
return pd.DataFrame(activity_list)
def dsp_win_func(w_inds, df, i, cols):
"""
Runs artefact rejection, PCA, and Hernandez DSP for a window of data
Arguments
---------
w_inds : numpy.ndarray
set of indexes for a given window
df : pandas.DataFrame
synchronised and frequency matched DataFrame with all data and labels
i : int
window index
cols : list
list of column str
Returns
-------
y_hat : pandas.DataFrame
estimated respiration rate from Hernandez method
y_out : pandas.DataFrame
max PSS frequency and median breathing rate from bioharness summary
file
"""
time = df['sec'].values
if w_inds[-1] == 0: return
w_df = df.iloc[w_inds]
t0, t1 = time[w_inds][0], time[w_inds][-1]
diff = time[w_inds[1:]] - time[w_inds[0:-1]]
mask = np.abs(diff)>20
diff_chk = np.any(mask)
if diff_chk:
return
data = w_df[cols].values
fs_est = 1/np.median(diff)
if fs_est > 70 and ('acc_x' in cols or 'gyro_x' in cols): fs = IMU_FS
elif fs_est < 70 and 'bvp' in cols: fs = PPG_FS
if reject_artefact((data-np.mean(data,axis=0))/np.std(data,axis=0)):
return
# DSP
pca = PCA(n_components=1, random_state=3)
# do hernandez sp on datacols for df
filt = hernandez_sp(data=data, fs=IMU_FS)[1]
# pca
pca_out = pca.fit_transform(filt)
std = StandardScaler().fit_transform(pca_out)
pred = get_max_frequency(std, fs=FS_RESAMPLE)
# get pss / br estimates
# x_time median, pss max_freq, br median
sm_out = w_df['BR'].values
ps_out = w_df['PSS'].values
x_vec_time = np.median(time[w_inds])
# fs = 1/np.mean(diff)
ps_out = pressure_signal_processing(ps_out, fs=fs)
ps_freq = get_max_frequency(ps_out, fs=IMU_FS)
y_tmp = np.array([x_vec_time, np.nanmedian(sm_out), ps_freq])
y_hat = pd.DataFrame([ {'sec': x_vec_time, 'pred': pred} ])
y_out = pd.DataFrame([y_tmp], columns=['sec', 'br', 'pss'])
return y_hat, y_out
# save evaluation metrics in single file that handles the models for the
# subject and config
class EvalHandler():
"""
Handles the evaluation metric for each subject and configuration.
...
Attributes
----------
y_true : numpy.ndarray
a numpy array of the respiration rate ground truth values from the
bioharness
y_pred : numpy.ndarray
a numpy array of the predicted respiration rate
subject : str
the subject in format Pilot01, S01 etc.
pfh : ProjectFileHandler
custom class detailing the directories, metafile, and configurations
mdl_str : str
a string to inform what model was used
overwrite : bool
overwrites the evaluations (default False)
Methods
-------
load_eval_history()
loads the evaluation file
save_eval_history()
saves the evaluation file
update_eval_history()
updates the evaluation file using the new entry if there is no matching
model or configuration for the given subject
"""
def __init__(self, y_true, y_pred, subject, pfh, mdl_str, overwrite=False):
self.subject = subject
self.config = pfh.config
self.parent_directory = join(DATA_DIR, 'subject_specific')
self.fset_id = pfh.fset_id
self.mdl_str = mdl_str
self.overwrite = overwrite
self.evals = Evaluation(y_true, y_pred)
entry = {'subject': self.subject,
'config_id': self.fset_id,
'mdl_str': self.mdl_str,
}
self.entry = {**entry, **self.config, **self.evals.get_evals()}
self.eval_history_file = join(self.parent_directory,
'eval_history.csv')
self.eval_hist = self.load_eval_history()
def load_eval_history(self):
if not exists(self.eval_history_file):
return None
else:
return pd.read_csv(self.eval_history_file)
def update_eval_history(self):
eval_hist = self.eval_hist
if eval_hist is None:
eval_hist = pd.DataFrame([self.entry])
else:
index_list = eval_hist[
(eval_hist['subject'] == self.entry['subject']) &\
(eval_hist['config_id'] == self.entry['config_id']) &\
(eval_hist['mdl_str'] == self.entry['mdl_str']) &\
(eval_hist['cpm'] == self.entry['cpm']) &\
(eval_hist['sens_list'] == self.entry['sens_list'])\
].index.tolist()
if len(index_list) == 0:
print("adding new entry")
eval_hist = eval_hist._append(self.entry, ignore_index=True)
elif index_list is not None:
eval_hist.loc[index_list[0]] = self.entry
self.eval_hist = eval_hist
def save_eval_history(self):
self.eval_hist.to_csv(self.eval_history_file, index=False)
# save evaluation metrics in single file that handles the models for the
# subject and config
class DSPEvalHandler():
"""
Handles the evaluation metric for each subject and DSP sensor.
...
Attributes
----------
y_true : numpy.ndarray
a numpy array of the respiration rate ground truth values from the
bioharness
y_pred : numpy.ndarray
a numpy array of the predicted respiration rate
subject : str
the subject in format Pilot01, S01 etc.
pfh : ProjectFileHandler
custom class detailing the directories, metafile, and configurations
sens_str : str
a string to inform what sensor was used
overwrite : bool
overwrites the evaluations (default False)
Methods
-------
load_eval_history()
loads the evaluation file
save_eval_history()
saves the evaluation file
update_eval_history()
updates the evaluation file using the new entry if there is no matching
model or configuration for the given subject
"""
def __init__(self, y_true, y_pred, subject, pfh, sens_str, overwrite=False):
self.subject = subject
self.config = pfh.config
self.parent_directory = join(DATA_DIR, 'subject_specific')
self.fset_id = pfh.fset_id
self.sens_str = sens_str
self.overwrite = overwrite
self.evals = Evaluation(y_true, y_pred)
entry = {'subject': self.subject,
'config_id': self.fset_id,
'sens_str': self.sens_str,
}
self.entry = {**entry, **self.config, **self.evals.get_evals()}
self.eval_history_file = join(self.parent_directory,
'dsp_eval_history.csv')
self.eval_hist = self.load_eval_history()
def load_eval_history(self):
if not exists(self.eval_history_file):
return None
else:
return pd.read_csv(self.eval_history_file)
def update_eval_history(self):
eval_hist = self.eval_hist
if eval_hist is None:
eval_hist = pd.DataFrame([self.entry])
else:
index_list = eval_hist[
(eval_hist['subject'] == self.entry['subject']) &\
(eval_hist['config_id'] == self.entry['config_id']) &\
(eval_hist['sens_str'] == self.entry['sens_str'])
].index.tolist()
if len(index_list) == 0:
print("adding new entry")
eval_hist = eval_hist._append(self.entry, ignore_index=True)
elif index_list is not None and self.overwrite:
eval_hist.loc[index_list[0]] = self.entry
self.eval_hist = eval_hist
def save_eval_history(self):
self.eval_hist.to_csv(self.eval_history_file, index=False)
def imu_rr_dsp(subject,
window_size=12,
window_shift=0.2,
lbl_str='pss',
overwrite=False,
train_len:int=3,
test_standing=False,
):
"""Loads, preprocesses, and performs Hernandez digital signal processing
pipeline on the selected subject. Uses the specified parameters. Runs on
both accelerometer and gyroscope.
Attributes
----------
subject: str
specify the subject code (i.e. 'Pilot02', 'S02')
window_size : float
a numpy array of the respiration rate ground truth values from the
bioharness
window_shift : float
a portion of the window size between 0 and 1
mdl_str : str
a string to infoa portion of the window size between 0 and 1rm what model was used
overwrite : bool
overwrites the evaluations, models, and graphs (default False)
test_standing : bool
boolean to use standing data
Returns
-------
None
"""
cal_str = 'cpm'
fs = IMU_FS
tmp = []
imu_cols = ['acc_x', 'acc_y', 'acc_z', 'gyro_x', 'gyro_y', 'gyro_z']
parent_directory_string = "imu_rr_dsp"
do_minirocket = False
use_tsfresh = False
overwrite_tsfresh = False
train_size = int(train_len)
config = {'window_size' : window_size,
'window_shift' : window_shift,
'lbl_str' : lbl_str,
'train_len' : train_len,
'test_standing' : test_standing,
'sens_list' : 'imu',
}
pfh = ProjectFileHandler(config)
pfh.set_home_directory(join(DATA_DIR, 'subject_specific', subject))
pfh.set_parent_directory(parent_directory_string)
id_check = pfh.get_id_from_config()
if id_check is None:
pfh.set_project_directory()
pfh.save_metafile()
else:
pfh.set_id(int(id_check))
pfh.set_project_directory()
print('Using pre-set data id: ', pfh.fset_id)
project_dir = pfh.project_directory
xsens_df = load_and_sync_xsens(subject, sens_list=['imu'])
activity_df = get_activity_log(subject)
event_df = get_respiration_log(subject)
cal_df = get_cal_data(event_df, xsens_df)
# include standing or not
test_df_tmp = get_test_data(cal_df, activity_df, xsens_df, test_standing)
test_df = pd.concat([df for df in test_df_tmp['data']], axis=0)
acc_dsp_df, acc_y_dsp_df = get_df_windows(test_df, dsp_win_func,
window_size=window_size,
window_shift=window_shift,
fs=fs,
cols=['acc_x', 'acc_y', 'acc_z'])
gyr_dsp_df, gyr_y_dsp_df = get_df_windows(test_df, dsp_win_func,
window_size=window_size,
window_shift=window_shift,
fs=fs,
cols=['gyro_x', 'gyro_y', 'gyro_z'])
acc_evals = Evaluation(acc_y_dsp_df[lbl_str], acc_dsp_df['pred'])
gyr_evals = Evaluation(gyr_y_dsp_df[lbl_str], gyr_dsp_df['pred'])
print("acc evals: \n", acc_evals.get_evals())
print("gyr evals: \n", gyr_evals.get_evals())
acc_eval = DSPEvalHandler(acc_evals.y_true.values.flatten(),
acc_evals.y_pred.values.flatten(),
subject,
pfh, 'acc', overwrite=overwrite)
acc_eval.update_eval_history()
acc_eval.save_eval_history()
gyr_eval = DSPEvalHandler(gyr_evals.y_true.values.flatten(),
gyr_evals.y_pred.values.flatten(),
subject,
pfh, 'gyro', overwrite=overwrite)
gyr_eval.update_eval_history()
gyr_eval.save_eval_history()
pp = PrettyPrinter()
pp.pprint(acc_eval.load_eval_history())
fig, ax = plt.subplots(2, 1)
ax[0].plot(acc_y_dsp_df[lbl_str].values);
ax[0].plot(acc_dsp_df['pred'].values)
ax[0].set_title("ACC")
ax[1].plot(gyr_y_dsp_df[lbl_str].values);
ax[1].plot(gyr_dsp_df['pred'].values)
ax[1].set_title("GYRO")
ax[1].legend([lbl_str, 'estimate'])
fig_dir = join(project_dir, 'figures')
if not exists(fig_dir): mkdir(fig_dir)
fig_title = '_'.join([subject, 'dsp'])
fig.savefig(join(fig_dir, fig_title+".png"))
plt.close()
def sens_rr_model(subject,
window_size=12,
window_shift=0.2,
lbl_str='pss',
mdl_str='knn',
overwrite=False,
feature_method='tsfresh',
train_len:int=3,
test_standing=False,
data_input:str='imu+bvp',
ntop_features:int=5
):
"""Loads, preprocesses, and trains a select model using the configured
settings.
Attributes
----------
subject: str
specify the subject code (i.e. 'Pilot02', 'S02')
window_size : float
a numpy array of the respiration rate ground truth values from the
bioharness
window_shift : float
a portion of the window size between 0 and 1
mdl_str : str
a string to infoa portion of the window size between 0 and 1rm what model was used
overwrite : bool
overwrites the evaluations, models, and graphs (default False)
feature_method : str
choose between 'minirocket', 'tsfresh', or 'None'
train_len : int
number of minutes to sample from, choose between 1 to 7
test_standing : bool
boolean to use standing data
data_input : str
sensors to use, choose from 'imu', 'bvp', 'imu+bvp'
Returns
------
None
"""
cal_str = 'cpm'
tmp = []
imu_cols = IMU_COLS
bvp_cols = ['bvp']
if 'imu' in data_input and 'bvp' in data_input:
data_cols = ['acc_x', 'acc_y', 'acc_z',
'gyro_x', 'gyro_y', 'gyro_z',
'bvp']
parent_directory_string = "imu-bvp_rr"
data_input = 'imu+bvp'
sens_list = ['imu', 'bvp']
fs = IMU_FS
elif 'imu' in data_input and not 'bvp' in data_input:
data_cols = ['acc_x', 'acc_y', 'acc_z',
'gyro_x', 'gyro_y', 'gyro_z',]
parent_directory_string = "imu_rr"
sens_list = ['imu']
fs = IMU_FS
elif not 'imu' in data_input and 'bvp' in data_input:
data_cols = ['bvp']
parent_directory_string = "bvp_rr"
sens_list = ['bvp']
fs = PPG_FS
do_minirocket = False
use_tsfresh = False
overwrite_tsfresh = overwrite
train_size = int(train_len)
minirocket = None
if feature_method == 'tsfresh':
use_tsfresh = True
elif feature_method == 'minirocket':
do_minirocket = True
config = {'window_size' : window_size,
'window_shift' : window_shift,
'lbl_str' : lbl_str,
'do_minirocket' : do_minirocket,
'use_tsfresh' : use_tsfresh,
'train_len' : train_len,
'test_standing' : test_standing,
'sens_list' : data_input
}
pfh = ProjectFileHandler(config)
pfh.set_home_directory(join(DATA_DIR, 'subject_specific', subject))
pfh.set_parent_directory(parent_directory_string)
id_check = pfh.get_id_from_config()
if id_check is None:
pfh.set_project_directory()
pfh.save_metafile()
else:
pfh.set_id(int(id_check))
pfh.set_project_directory()
print('Using pre-set data id: ', pfh.fset_id)
project_dir = pfh.project_directory
xsens_df = load_and_sync_xsens(subject, sens_list=sens_list)
activity_df = get_activity_log(subject).reset_index(drop=True)
event_df = get_respiration_log(subject)
cal_df = get_cal_data(event_df, xsens_df)
# include standing or not
test_df_tmp = get_test_data(cal_df, activity_df, xsens_df, test_standing)
test_df = pd.concat([df for df in test_df_tmp['data']], axis=0)
y_cols = ['sec', 'br', 'pss', 'cpm']
if use_tsfresh:
cal_df_list = []
top_cpm_features = {}
for index, row in cal_df.iterrows():
data = load_tsfresh(row['data'],
pfh.home_directory,
window_size=window_size,
window_shift=window_shift,
fs=fs,
overwrite=overwrite_tsfresh,
data_cols=data_cols,
prefix=f"calcpm_{row['cpm']}"
)
m_data_cols = data.columns.values[3:]
rel_df = relevance.calculate_relevance_table(
data[m_data_cols], data['pss'])
params = []
if 'imu' in sens_list:
tmp = [
val for val in rel_df['feature'] if 'acc' in val or
'gyr' in val
]
params += tmp[:ntop_features]
if 'bvp' in sens_list:
tmp = [val for val in rel_df['feature'] if 'bvp' in val]
params += tmp[:ntop_features]
assert params is not None, "Invalid selection of parameters"
top_cpm_features[row['cpm']] = params
cal_df_list.append({'cpm': row['cpm'], 'data': data})
param_df = pd.DataFrame.from_dict(top_cpm_features, orient='index')
top_cpm_params = param_df.columns.values
arr = param_df[top_cpm_params].values.flatten()
hist_df = pd.DataFrame.from_dict(Counter(arr), orient='index')
feature_strings = get_parameters_from_feature_string(
hist_df.index.values.tolist())
cal_df = pd.DataFrame(cal_df_list)
# Get the relevance for tables cal and extract top 20
test_df = load_tsfresh(test_df,
pfh.home_directory,
window_size=window_size,
window_shift=window_shift,
fs=fs,
overwrite=overwrite_tsfresh,
data_cols=data_cols,
prefix='test',
kind_to_fc_parameters=feature_strings
)
chosen_cols = [col for col in test_df.columns if col in
hist_df.index.tolist()+y_cols]
test_df = test_df[chosen_cols]
else:
x_test_df, y_test_df = get_df_windows(
test_df, df_win_task, window_size=window_size,
window_shift=window_shift, fs=fs, cols=data_cols)
for combi in combinations(cal_df[cal_str].values, train_len):
combi_str = "-".join([str(x) for x in combi])
pfh.config[cal_str] = combi_str
marker = f'{parent_directory_string}_{subject}_id{pfh.fset_id}'\
f'_combi{combi_str}'
print(marker)
train_df_list = []
for cpm in combi:
df = cal_df[cal_df[cal_str] == cpm]
data_df = df['data'].iloc[0]
data_df['cpm'] = cpm
train_df_list.append(data_df)
train_df = pd.concat(train_df_list)
assert np.isin(train_df.sec.values, test_df.sec.values).any()==False,\
"overlapping test and train data"
print("train")
print(train_df.shape)
print("test")
print(test_df.shape)
if do_minirocket:
x_train_df, y_train_df = get_df_windows(train_df,
df_win_task,
window_size=window_size,
window_shift=window_shift,
fs=fs,
cols=data_cols
)
x_train = make_windows_from_id(x_train_df, data_cols)
y_train = y_train_df['cpm'].values.reshape(-1, 1)
x_test = make_windows_from_id(x_test_df, data_cols)
y_test = y_test_df[lbl_str].values.reshape(-1, 1)
# x_train = y_train_df['hr_est'].values.reshape(-1, 1)
# x_test = y_test_df['hr_est'].values.reshape(-1, 1)
x_train = np.swapaxes(x_train, 1, 2)
x_test = np.swapaxes(x_test, 1, 2)
directory = join(project_dir, '_'.join([mdl_str, marker]))
minirocket_fname = join(directory, "minirocket.joblib")
if not overwrite and exists(minirocket_fname):
with open(minirocket_fname, 'rb') as mfile:
minirocket = load(mfile)
x_train = minirocket.transform(x_train)
print("loaded minirocket...")
else:
minirocket = MiniRocketMultivariate()
x_train = minirocket.fit_transform(x_train)
x_test = minirocket.transform(x_test)
if overwrite or not exists(minirocket_fname):
if not exists(directory): makedirs(directory)
with open(minirocket_fname, 'wb') as mfile:
dump(minirocket, mfile)
print("saved new minirocket...")
elif use_tsfresh:
y_cols = ['sec', 'br', 'pss', 'cpm']
x_cols = [col for col in test_df.columns.values if col not in y_cols]
x_test = test_df[x_cols].values
y_test = test_df[lbl_str].values.reshape(-1, 1)
y_test_df = test_df[y_cols[:-1]]
x_train = train_df[x_cols].values
y_train = train_df['cpm'].values.reshape(-1, 1)
else:
x_train_df, y_train_df = get_df_windows(train_df,
df_win_task,
window_size=window_size,
window_shift=window_shift,
fs=fs,
cols=data_cols,
)
x_train = make_windows_from_id(x_train_df, data_cols)
x_test = make_windows_from_id(x_test_df, data_cols)
y_train = y_train_df['cpm'].values.reshape(-1, 1)
y_test = y_test_df[lbl_str].values.reshape(-1, 1)
transforms, model = model_training(mdl_str, x_train, y_train,
marker, validation_data=None,
overwrite=overwrite,
is_regression=True,
project_directory=project_dir,
window_size=int(window_size*fs),
extra_train=200,
poly_deg=1
)
if transforms is not None:
x_test = transforms.transform(x_test)
preds = model.predict(x_test)
eval_handle = EvalHandler(y_test.flatten(), preds.flatten(), subject,
pfh, mdl_str, overwrite=overwrite)
eval_handle.update_eval_history()
eval_handle.save_eval_history()
pp = PrettyPrinter()
pp.pprint(eval_handle.load_eval_history())
fig, ax = plt.subplots(2, 1, figsize=(7.3, 4.5))
fig_title = '_'.join([mdl_str, data_input, subject]+[combi_str])
fig.suptitle(fig_title)
ax[0].plot(y_test)
ax[0].plot(preds)
ax[0].set_title('raw')
if lbl_str == 'pss':
br = y_test_df['br'].values
ax[1].plot(movingaverage(y_test, 12),
color='tab:blue')
ax[1].plot(br, 'k')
ax[1].plot(movingaverage(preds, 12),
color='tab:orange')
ax[1].legend([lbl_str, 'br', 'pred'])
else:
ax[1].plot(y_test, 'k')
ax[1].plot(movingaverage(preds, 12), color='tab:orange')
ax[1].legend([lbl_str, 'pred'])
ax[1].set_title('smoothened')
fig_dir = join(project_dir, 'figures')
if not exists(fig_dir): mkdir(fig_dir)
if subject == 'S02':
sec = y_test_df['sec'].values
activity_df2 = get_activity_log(subject+'-repeat')
event_df2 = get_respiration_log(subject+'-repeat')
second_cal_df = get_cal_data(event_df2, xsens_df)\
.reset_index(drop=True)
sec = y_test_df['sec'].values
activity_df2 = get_activity_log(subject+'-repeat')
event_df2 = get_respiration_log(subject+'-repeat')
second_cal_df = get_cal_data(event_df2, xsens_df)\
.reset_index(drop=True)
fmt = "%d/%m/%Y %H:%M:%S"
activity_sec0 = datetime.strptime(
activity_df2['Timestamps'].iloc[0], fmt).timestamp()
activity_sec1 = datetime.strptime(
activity_df2['Timestamps'].iloc[-1], fmt).timestamp()
ind0 = np.argmin(np.abs(sec - activity_sec0))
ind1 = np.argmin(np.abs(sec - activity_sec1))
ax[0].axvline(ind0, c='r', linestyle='--')
ax[0].axvline(ind1, c='r', linestyle='--')
ax[1].axvline(ind0, c='r', linestyle='--')
ax[1].axvline(ind1, c='r', linestyle='--')
fig2, ax2 = plt.subplots(2, 1, sharex=True)
cal_sec0 = second_cal_df.iloc[-7]['data']['sec'].iloc[0]
cal_sec1 = second_cal_df.iloc[-1]['data']['sec'].iloc[-1]
calind0 = np.argmin(np.abs(sec - cal_sec0))
calind1 = np.argmin(np.abs(sec - cal_sec1))
ax2[0].plot(sec[calind0:], y_test[calind0:])
ax2[0].plot(sec[calind0:],preds[calind0:])
ax2[0].set_title('raw')
ax2[1].plot(sec[calind0:],
movingaverage(y_test, 12)[calind0:],
color='tab:blue')
ax2[1].plot(sec[calind0:], br[calind0:], 'k')
ax2[1].plot(sec[calind0:],
movingaverage(preds, 12)[calind0:],
color='tab:orange')
ax2[1].legend([lbl_str, 'br', 'pred'])
ll = len(second_cal_df)
for i in range(ll-7, ll):
cal_sec0 = second_cal_df.iloc[i]['data']['sec'].iloc[0]
cal_sec1 = second_cal_df.iloc[i]['data']['sec'].iloc[-1]
calind0 = np.argmin(np.abs(sec - cal_sec0))
calind1 = np.argmin(np.abs(sec - cal_sec1))
ax2[0].axvline(sec[calind0], c='g', linestyle='--')
ax2[0].axvline(sec[calind1], c='g', linestyle='--')
ax2[1].axvline(sec[calind0], c='g', linestyle='--')
ax2[1].axvline(sec[calind1], c='g', linestyle='--')
def pressure_ax_plot(func, win_size=12, win_shift=0.2, fs=120):
dt = test_df.sec
t = dt.map(lambda x: datetime.fromtimestamp(x))
pss_sig = test_df.PSS.values
processed_wins = vsw(pss_sig, len(pss_sig),
sub_window_size=int(win_size*fs),
stride_size=int(win_size*win_shift*fs))
processed_wins = map(func, processed_wins)
ps_freq = [get_max_frequency(ps_out, fs=fs) for ps_out in
processed_wins]
ft = vsw(dt.values, len(dt),
sub_window_size=int(win_size*fs),
stride_size=int(win_size*win_shift*fs))
fig, axs = plt.subplots(3, 1, sharex=True)
# axs[0].get_shared_x_axes().join(axs[0], axs[1])
axs[0].plot(t, pss_sig)
axs[1].plot(t, func(pss_sig))
mtime = map(lambda x: datetime.fromtimestamp(x),
ft.mean(axis=1))
freqdt = np.array([t for t in mtime])
tt = y_test_df.sec.map(lambda x:
datetime.fromtimestamp(x)).values
axs[2].plot(freqdt, ps_freq)
axs[2].plot(tt, y_test_df.pss.values)
# fig.savefig(join(fig_dir, fig_title+"cal_check.png"))
fig2.savefig(join(fig_dir, fig_title+"cal_check.png"))
# func = partial(pressure_signal_processing, fs=fs)
# pressure_ax_plot(func)
fig.savefig(join(fig_dir, fig_title+".png"))
plt.close('all')
def arg_parser():
"""Returns arguments in a Namespace to configure the subject specific model
"""
parser = argparse.ArgumentParser()
parser.add_argument("-m", '--model', type=str,
default='linreg',
choices=['linreg', 'ard', 'xgboost', 'knn',
'svr', 'cnn1d', 'fnn', 'lstm', 'ridge',
'elastic'],
)
parser.add_argument("-s", '--subject', type=int,
default=1,
choices=list(range(1,N_SUBJECT_MAX+1))+[-1],
)
parser.add_argument("-f", '--feature_method', type=str,
default='minirocket',
choices=['tsfresh', 'minirocket', 'None']
)
parser.add_argument("-o", '--overwrite', type=int,
default=0,
)
parser.add_argument('--win_size', type=int,
default=20,
)
parser.add_argument('--win_shift', type=float,
default=0.05,
)
parser.add_argument('-l', '--lbl_str', type=str,
default='pss',
choices=['pss', 'br']
)
parser.add_argument('-tl', '--train_len', type=int,
default=5,
help='minutes of data to use for calibration'
)
parser.add_argument('-d', '--data_input', type=str,
default='imu',
help='imu, bvp, imu+bvp: select data cols for input'
)
parser.add_argument('-ts', '--test_standing', type=int,
default=1,
help='1 or 0 input, choose if standing data will be '\
'recorded or not'
)
parser.add_argument('--method', type=str,
default='ml',
help="choose between 'ml' or 'dsp' methods for"\
"regression",
choices=['ml', 'dsp']
)
args = parser.parse_args()
return args
if __name__ == '__main__':
np.random.seed(100)
args = arg_parser()
# Load command line arguments
mdl_str = args.model
subject = args.subject
feature_method = args.feature_method
window_size = args.win_size
window_shift = args.win_shift
lbl_str = args.lbl_str
train_len = args.train_len
overwrite = args.overwrite
data_input = args.data_input
test_standing = args.test_standing
method = args.method
print(args)
assert train_len>0,"--train_len must be an integer greater than 0"
assert train_len <= 7,"--train_len must be an integer less than 8"
subject_pre_string = 'S' # Pilot / S
if subject > 0 and method == 'ml':
subject = subject_pre_string+str(subject).zfill(2)
sens_rr_model(subject,
window_size=window_size,
window_shift=window_shift,
lbl_str=lbl_str,
mdl_str=mdl_str,
overwrite=overwrite,
feature_method=feature_method,
train_len=train_len,
test_standing=test_standing,
data_input=data_input,
)
elif subject <= 0 and method == 'ml':
subjects = [subject_pre_string+str(i).zfill(2) for i in \
range(1, N_SUBJECT_MAX+1)]
rr_func = partial(sens_rr_model,
window_size=window_size,
window_shift=window_shift,
lbl_str=lbl_str,
mdl_str=mdl_str,
overwrite=overwrite,
feature_method=feature_method,
train_len=train_len,
test_standing=test_standing,
data_input=data_input,
)
for subject in subjects:
rr_func(subject)
elif subject > 0 and method == 'dsp':
subject = subject_pre_string+str(subject).zfill(2)
imu_rr_dsp(subject,
window_size=window_size,
window_shift=window_shift,
lbl_str=lbl_str,
overwrite=overwrite,
train_len=train_len,
test_standing=test_standing)
elif subject <= 0 and method == 'dsp':
subjects = [subject_pre_string+str(i).zfill(2) for i in \
range(1, N_SUBJECT_MAX+1)]
func = partial(imu_rr_dsp, window_size=window_size,
window_shift=window_shift,
lbl_str=lbl_str,
overwrite=overwrite,
train_len=train_len,
test_standing=test_standing)
for subject in subjects:
func(subject)
elif subject <= 0 and method == 'ml':
subjects = [subject_pre_string+str(i).zfill(2) for i in \
range(1, N_SUBJECT_MAX+1)]
if 'bvp' in data_input:
# Missing bvp for S03, S04
subjects = [sbf for sbj in subjects if sbj in ['S03']]
rr_func = partial(sens_rr_model,
window_size=window_size,
window_shift=window_shift,
lbl_str=lbl_str,
mdl_str=mdl_str,
overwrite=overwrite,
feature_method=feature_method,
train_len=train_len,
test_standing=test_standing,
data_input=data_input,
)
for subject in subjects:
rr_func(subject)
print(args)