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from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from regress_rr import load_tsfresh
from os.path import join
from os import makedirs
import ipdb
import joblib
import pandas as pd
from config import *
from regress_rr import get_activity_log
from modules.evaluations import Evaluation
from modules.digitalsignalprocessing import (
movingaverage, butter_lowpass_filter)
from sklearn.preprocessing import PolynomialFeatures, LabelEncoder
plt.close('all')
plt.rcParams.update({'figure.titlesize' : 8,
'axes.titlesize' : 7,
'axes.labelsize' : 7,
'xtick.labelsize' : 6,
'ytick.labelsize' : 6,
'legend.fontsize' : 6,
'legend.title_fontsize' : 6,
})
window_size = 20
window_shift = 0.05
# get the test data for the subject
# load imu, bvp, and imu+bvp data for the subject
# get the lin reg data for the subject
# plot the pss vs the linear regression data for the subject
# highlight standing periods
sbj = 'S01'
sens_list = ['imu', 'bvp', 'imu-bvp']
# cfg_ids = [4, 0, 0]
cfg_ids = [5, 11, 4]
lbl_str = 'pss'
mdl_dir = join(DATA_DIR, 'subject_specific', sbj)
mdl_file = 'linr_model.joblib'
tsfresh_dir = join(
mdl_dir,
'tsfresh__winsize_{0}__winshift_{1}'.format(window_size, window_shift)
)
combi_strs = ['combi7.0-10.0-12.0-15.0-17.0']*3
def get_model(sens, sbj, cfg_id, combi):
m_dir = f'linreg_{sens}_rr_{sbj}_id{cfg_id}_{combi}'
mdl_fname = join(mdl_dir, sens+'_rr', str(cfg_id).zfill(2), m_dir, mdl_file)
return joblib.load(mdl_fname)
def get_test_data():
test_fname = 'test__winsize_{0}__winshift_{1}__tsfresh.pkl'.format(
window_size, window_shift)
test_df = pd.read_pickle(join(tsfresh_dir, test_fname))
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]
y_test = test_df[lbl_str].values.reshape(-1, 1)
time = test_df['sec']
return x_test, y_test, time
if __name__ == '__main__':
activity_log = get_activity_log(sbj)
standing_mask = activity_log['Activity'] == 'standing'
standing_log = activity_log[standing_mask]
x_test, y_test, time = get_test_data()
y_out = {}
metrics_dict = {}
x_test_cols = x_test.columns
y_test = movingaverage(y_test, 8)
cm = 1/2.54
fig, axs = plt.subplots(figsize=(14*cm, 7.5*cm), dpi=300)
axs.plot(y_test, label='ground-truth', linewidth=1, c='k');
standing_wins = 10*60
for sens, cfg_id, combi_str in zip(sens_list, cfg_ids, combi_strs):
model = get_model(sens, sbj, cfg_id, combi_str)
if sens == 'imu':
cols = [col for col in x_test_cols if 'acc' in col or 'gyr' in col]
elif sens == 'bvp':
cols = [col for col in x_test_cols if 'bvp' in col]
else:
cols = x_test.columns
poly = PolynomialFeatures(1)
x_test_sens = x_test[cols]
x_test_sens = poly.fit_transform(x_test_sens)
y_pred = model.predict(x_test_sens)
y_pred = movingaverage(y_pred, 64)
evals = Evaluation(y_test.flatten(), y_pred.flatten())
metrics_dict[sens]=evals.get_evals()
axs.plot(y_pred, label=sens.upper(), linewidth=1)
# axs.plot(np.abs(y_test - y_pred), label=sens.upper())
y0 = axs.get_ylim()[-1]
# y1 = axs[1].get_ylim()[-1]
fmt = "%d/%m/%Y %H:%M:%S"
x = np.arange(len(y_test))
for start_, end_ in zip(
standing_log.iloc[::2].iterrows(), standing_log.iloc[1::2].iterrows()
):
start = start_[1]
end = end_[1]
start_sec = datetime.strptime(start['Timestamps'], fmt).timestamp()
end_sec = datetime.strptime(end['Timestamps'], fmt).timestamp()
mask = (time >= start_sec) & (time <= end_sec)
axs.fill_between(x, y0, where=mask, facecolor='k', alpha=0.2)
# axs[1].fill_between(x, y1, where=mask, facecolor='k', alpha=0.2)
diff = np.diff(time)
diff = np.insert(diff, 0, 0)
new_day_idx = diff.argmax()
axs.axvline(new_day_idx, c='r', alpha=0.6, linewidth=1)
# axs[1].axvline(new_day_idx, c='r')
print(metrics_dict)
axs.legend(ncols=4, prop={'size': 6})
# fig.suptitle("RR Predictions during Rest Standing and Sitting")
axs.set_title("Multi-Modal RR Predictions during Rest Standing and Sitting")
# axs[1].set_title("Absolute Error over Time")
axs.set_xlabel("Time (indices)")
axs.set_ylabel("Respiration Rate (CPM)")
# axs[1].set_ylabel("Abs. Error")
fig.savefig('S01cal.png')