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DeepFilter_main.py
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# -*- coding: utf-8 -*-
#============================================================
#
# Deep Learning BLW Filtering
# Main
#
# author: Francisco Perdigon Romero
# email: [email protected]
# github id: fperdigon
#
#===========================================================
import _pickle as pickle
from datetime import datetime
import time
import numpy as np
from utils.metrics import MAD, SSD, PRD, COS_SIM
from utils import visualization as vs
from Data_Preparation import data_preparation as dp
from digitalFilters.dfilters import FIR_test_Dataset, IIR_test_Dataset
from deepFilter.dl_pipeline import train_dl, test_dl
if __name__ == "__main__":
dl_experiments = [
'DRNN',
'FCN-DAE',
'Vanilla L',
'Vanilla NL',
'Multibranch LANL',
'Multibranch LANLD'
]
noise_versions = [1, 2]
for nv in noise_versions:
# Data_Preparation() function assumes that QT database and Noise Stress Test Database are uncompresed
# inside a folder called data
Dataset = dp.Data_Preparation(noise_version=nv)
# Save dataset
with open('data/dataset_nv' + str(nv) + '.pkl', 'wb') as output: # Overwrites any existing file.
pickle.dump(Dataset, output)
print('Dataset saved')
# Load dataset
with open('data/dataset_nv' + str(nv) + '.pkl', 'rb') as input:
Dataset = pickle.load(input)
train_time_list = []
test_time_list = []
for experiment in range(len(dl_experiments)):
start_train = datetime.now()
train_dl(Dataset, dl_experiments[experiment])
end_train = datetime.now()
train_time_list.append(end_train - start_train)
start_test = datetime.now()
[X_test, y_test, y_pred] = test_dl(Dataset, dl_experiments[experiment])
end_test = datetime.now()
test_time_list.append(end_test - start_test)
test_results = [X_test, y_test, y_pred]
# Save Results
with open('test_results_' + dl_experiments[experiment] + '_nv' + str(nv) + '.pkl', 'wb') as output: # Overwrites any existing file.
pickle.dump(test_results, output)
print('Results from experiment ' + dl_experiments[experiment] + '_nv' + str(nv) + ' saved')
time.sleep(60)
# Classical Filters
# FIR
start_test = datetime.now()
[X_test_f, y_test_f, y_filter] = FIR_test_Dataset(Dataset)
end_test = datetime.now()
train_time_list.append(0)
test_time_list.append(end_test - start_test)
test_results_FIR = [X_test_f, y_test_f, y_filter]
# Save FIR filter results
with open('test_results_FIR_nv' + str(nv) + '.pkl', 'wb') as output: # Overwrites any existing file.
pickle.dump(test_results_FIR, output)
print('Results from experiment FIR filter nv ' + str(nv) + ' saved')
# IIR
start_test = datetime.now()
[X_test_f, y_test_f, y_filter] = IIR_test_Dataset(Dataset)
end_test = datetime.now()
train_time_list.append(0)
test_time_list.append(end_test - start_test)
test_results_IIR = [X_test_f, y_test_f, y_filter]
# Save IIR filter results
with open('test_results_IIR_nv' + str(nv) + '.pkl', 'wb') as output: # Overwrites any existing file.
pickle.dump(test_results_IIR, output)
print('Results from experiment IIR filter nv ' + str(nv) + ' saved')
# Saving timing list
timing = [train_time_list, test_time_list]
with open('timing_nv' + str(nv) + '.pkl', 'wb') as output: # Overwrites any existing file.
pickle.dump(timing, output)
print('Timing nv ' + str(nv) + ' saved')
####### LOAD EXPERIMENTS #######
# Load timing
with open('timing_nv1.pkl', 'rb') as input:
timing_nv1 = pickle.load(input)
[train_time_list_nv1, test_time_list_nv1] = timing_nv1
with open('timing_nv2.pkl', 'rb') as input:
timing_nv2 = pickle.load(input)
[train_time_list_nv2, test_time_list_nv2] = timing_nv2
train_time_list = []
test_time_list = []
for i in range(len(train_time_list_nv1)):
train_time_list.append(train_time_list_nv1[i] + train_time_list_nv2[i])
for i in range(len(test_time_list_nv1)):
test_time_list.append(test_time_list_nv1[i] + test_time_list_nv2[i])
timing = [train_time_list, test_time_list]
# Load Results DRNN
with open('test_results_' + dl_experiments[0] + '_nv1.pkl', 'rb') as input:
test_DRNN_nv1 = pickle.load(input)
with open('test_results_' + dl_experiments[0] + '_nv2.pkl', 'rb') as input:
test_DRNN_nv2 = pickle.load(input)
test_DRNN = [np.concatenate((test_DRNN_nv1[0], test_DRNN_nv2[0])),
np.concatenate((test_DRNN_nv1[1], test_DRNN_nv2[1])),
np.concatenate((test_DRNN_nv1[2], test_DRNN_nv2[2]))]
# Load Results FCN_DAE
with open('test_results_' + dl_experiments[1] + '_nv1.pkl', 'rb') as input:
test_FCN_DAE_nv1 = pickle.load(input)
with open('test_results_' + dl_experiments[1] + '_nv2.pkl', 'rb') as input:
test_FCN_DAE_nv2 = pickle.load(input)
test_FCN_DAE = [np.concatenate((test_FCN_DAE_nv1[0], test_FCN_DAE_nv2[0])),
np.concatenate((test_FCN_DAE_nv1[1], test_FCN_DAE_nv2[1])),
np.concatenate((test_FCN_DAE_nv1[2], test_FCN_DAE_nv2[2]))]
# Load Results Vanilla L
with open('test_results_' + dl_experiments[2] + '_nv1.pkl', 'rb') as input:
test_Vanilla_L_nv1 = pickle.load(input)
with open('test_results_' + dl_experiments[2] + '_nv2.pkl', 'rb') as input:
test_Vanilla_L_nv2 = pickle.load(input)
test_Vanilla_L = [np.concatenate((test_Vanilla_L_nv1[0], test_Vanilla_L_nv2[0])),
np.concatenate((test_Vanilla_L_nv1[1], test_Vanilla_L_nv2[1])),
np.concatenate((test_Vanilla_L_nv1[2], test_Vanilla_L_nv2[2]))]
# Load Results Exp Vanilla NL
with open('test_results_' + dl_experiments[3] + '_nv1.pkl', 'rb') as input:
test_Vanilla_NL_nv1 = pickle.load(input)
with open('test_results_' + dl_experiments[3] + '_nv2.pkl', 'rb') as input:
test_Vanilla_NL_nv2 = pickle.load(input)
test_Vanilla_NL = [np.concatenate((test_Vanilla_NL_nv1[0], test_Vanilla_NL_nv2[0])),
np.concatenate((test_Vanilla_NL_nv1[1], test_Vanilla_NL_nv2[1])),
np.concatenate((test_Vanilla_NL_nv1[2], test_Vanilla_NL_nv2[2]))]
# Load Results Multibranch LANL
with open('test_results_' + dl_experiments[4] + '_nv1.pkl', 'rb') as input:
test_Multibranch_LANL_nv1 = pickle.load(input)
with open('test_results_' + dl_experiments[4] + '_nv2.pkl', 'rb') as input:
test_Multibranch_LANL_nv2 = pickle.load(input)
test_Multibranch_LANL = [np.concatenate((test_Multibranch_LANL_nv1[0], test_Multibranch_LANL_nv2[0])),
np.concatenate((test_Multibranch_LANL_nv1[1], test_Multibranch_LANL_nv2[1])),
np.concatenate((test_Multibranch_LANL_nv1[2], test_Multibranch_LANL_nv2[2]))]
# Load Results Multibranch LANLD
with open('test_results_' + dl_experiments[5] + '_nv1.pkl', 'rb') as input:
test_Multibranch_LANLD_nv1 = pickle.load(input)
with open('test_results_' + dl_experiments[5] + '_nv2.pkl', 'rb') as input:
test_Multibranch_LANLD_nv2 = pickle.load(input)
test_Multibranch_LANLD = [np.concatenate((test_Multibranch_LANLD_nv1[0], test_Multibranch_LANLD_nv2[0])),
np.concatenate((test_Multibranch_LANLD_nv1[1], test_Multibranch_LANLD_nv2[1])),
np.concatenate((test_Multibranch_LANLD_nv1[2], test_Multibranch_LANLD_nv2[2]))]
# Load Result FIR Filter
with open('test_results_FIR_nv1.pkl', 'rb') as input:
test_FIR_nv1 = pickle.load(input)
with open('test_results_FIR_nv2.pkl', 'rb') as input:
test_FIR_nv2 = pickle.load(input)
test_FIR = [np.concatenate((test_FIR_nv1[0], test_FIR_nv2[0])),
np.concatenate((test_FIR_nv1[1], test_FIR_nv2[1])),
np.concatenate((test_FIR_nv1[2], test_FIR_nv2[2]))]
# Load Result IIR Filter
with open('test_results_IIR_nv1.pkl', 'rb') as input:
test_IIR_nv1 = pickle.load(input)
with open('test_results_IIR_nv2.pkl', 'rb') as input:
test_IIR_nv2 = pickle.load(input)
test_IIR = [np.concatenate((test_IIR_nv1[0], test_IIR_nv2[0])),
np.concatenate((test_IIR_nv1[1], test_IIR_nv2[1])),
np.concatenate((test_IIR_nv1[2], test_IIR_nv2[2]))]
####### Calculate Metrics #######
print('Calculating metrics ...')
# DL Metrics
# Exp FCN-DAE
[X_test, y_test, y_pred] = test_DRNN
SSD_values_DL_DRNN = SSD(y_test, y_pred)
MAD_values_DL_DRNN = MAD(y_test, y_pred)
PRD_values_DL_DRNN = PRD(y_test, y_pred)
COS_SIM_values_DL_DRNN = COS_SIM(y_test, y_pred)
# Exp FCN-DAE
[X_test, y_test, y_pred] = test_FCN_DAE
SSD_values_DL_FCN_DAE = SSD(y_test, y_pred)
MAD_values_DL_FCN_DAE = MAD(y_test, y_pred)
PRD_values_DL_FCN_DAE = PRD(y_test, y_pred)
COS_SIM_values_DL_FCN_DAE = COS_SIM(y_test, y_pred)
# Vanilla L
[X_test, y_test, y_pred] = test_Vanilla_L
SSD_values_DL_exp_1 = SSD(y_test, y_pred)
MAD_values_DL_exp_1 = MAD(y_test, y_pred)
PRD_values_DL_exp_1 = PRD(y_test, y_pred)
COS_SIM_values_DL_exp_1 = COS_SIM(y_test, y_pred)
# Vanilla_NL
[X_test, y_test, y_pred] = test_Vanilla_NL
SSD_values_DL_exp_2 = SSD(y_test, y_pred)
MAD_values_DL_exp_2 = MAD(y_test, y_pred)
PRD_values_DL_exp_2 = PRD(y_test, y_pred)
COS_SIM_values_DL_exp_2 = COS_SIM(y_test, y_pred)
# Multibranch_LANL
[X_test, y_test, y_pred] = test_Multibranch_LANL
SSD_values_DL_exp_3 = SSD(y_test, y_pred)
MAD_values_DL_exp_3 = MAD(y_test, y_pred)
PRD_values_DL_exp_3 = PRD(y_test, y_pred)
COS_SIM_values_DL_exp_3 = COS_SIM(y_test, y_pred)
# Multibranch_LANLD
[X_test, y_test, y_pred] = test_Multibranch_LANLD
SSD_values_DL_exp_4 = SSD(y_test, y_pred)
MAD_values_DL_exp_4 = MAD(y_test, y_pred)
PRD_values_DL_exp_4 = PRD(y_test, y_pred)
COS_SIM_values_DL_exp_4 = COS_SIM(y_test, y_pred)
# Digital Filtering
# FIR Filtering Metrics
[X_test, y_test, y_filter] = test_FIR
SSD_values_FIR = SSD(y_test, y_filter)
MAD_values_FIR = MAD(y_test, y_filter)
PRD_values_FIR = PRD(y_test, y_filter)
COS_SIM_values_FIR = COS_SIM(y_test, y_filter)
# IIR Filtering Metrics (Best)
[X_test, y_test, y_filter] = test_IIR
SSD_values_IIR = SSD(y_test, y_filter)
MAD_values_IIR = MAD(y_test, y_filter)
PRD_values_IIR = PRD(y_test, y_filter)
COS_SIM_values_IIR = COS_SIM(y_test, y_filter)
####### Results Visualization #######
SSD_all = [SSD_values_FIR,
SSD_values_IIR,
SSD_values_DL_FCN_DAE,
SSD_values_DL_DRNN,
SSD_values_DL_exp_1,
SSD_values_DL_exp_2,
SSD_values_DL_exp_3,
SSD_values_DL_exp_4,
]
MAD_all = [MAD_values_FIR,
MAD_values_IIR,
MAD_values_DL_FCN_DAE,
MAD_values_DL_DRNN,
MAD_values_DL_exp_1,
MAD_values_DL_exp_2,
MAD_values_DL_exp_3,
MAD_values_DL_exp_4,
]
PRD_all = [PRD_values_FIR,
PRD_values_IIR,
PRD_values_DL_FCN_DAE,
PRD_values_DL_DRNN,
PRD_values_DL_exp_1,
PRD_values_DL_exp_2,
PRD_values_DL_exp_3,
PRD_values_DL_exp_4,
]
COS_SIM_all = [COS_SIM_values_FIR,
COS_SIM_values_IIR,
COS_SIM_values_DL_FCN_DAE,
COS_SIM_values_DL_DRNN,
COS_SIM_values_DL_exp_1,
COS_SIM_values_DL_exp_2,
COS_SIM_values_DL_exp_3,
COS_SIM_values_DL_exp_4,
]
Exp_names = ['FIR Filter', 'IIR Filter'] + dl_experiments
metrics = ['SSD', 'MAD', 'PRD', 'COS_SIM']
metric_values = [SSD_all, MAD_all, PRD_all, COS_SIM_all]
# Metrics table
vs.generate_table(metrics, metric_values, Exp_names)
# Timing table
timing_var = ['training', 'test']
vs.generate_table_time(timing_var, timing, Exp_names, gpu=True)
################################################################################################################
# Segmentation by noise amplitude
rnd_test = np.load('rnd_test.npy')
rnd_test = np.concatenate([rnd_test, rnd_test])
segm = [0.2, 0.6, 1.0, 1.5, 2.0] # real number of segmentations is len(segmentations) - 1
SSD_seg_all = []
MAD_seg_all = []
PRD_seg_all = []
COS_SIM_seg_all = []
for idx_exp in range(len(Exp_names)):
SSD_seg = [None] * (len(segm) - 1)
MAD_seg = [None] * (len(segm) - 1)
PRD_seg = [None] * (len(segm) - 1)
COS_SIM_seg = [None] * (len(segm) - 1)
for idx_seg in range(len(segm) - 1):
SSD_seg[idx_seg] = []
MAD_seg[idx_seg] = []
PRD_seg[idx_seg] = []
COS_SIM_seg[idx_seg] = []
for idx in range(len(rnd_test)):
# Object under analysis (oua)
# SSD
oua = SSD_all[idx_exp][idx]
if rnd_test[idx] > segm[idx_seg] and rnd_test[idx] < segm[idx_seg + 1]:
SSD_seg[idx_seg].append(oua)
# MAD
oua = MAD_all[idx_exp][idx]
if rnd_test[idx] > segm[idx_seg] and rnd_test[idx] < segm[idx_seg + 1]:
MAD_seg[idx_seg].append(oua)
# PRD
oua = PRD_all[idx_exp][idx]
if rnd_test[idx] > segm[idx_seg] and rnd_test[idx] < segm[idx_seg + 1]:
PRD_seg[idx_seg].append(oua)
# COS SIM
oua = COS_SIM_all[idx_exp][idx]
if rnd_test[idx] > segm[idx_seg] and rnd_test[idx] < segm[idx_seg + 1]:
COS_SIM_seg[idx_seg].append(oua)
# Processing the last index
# SSD
SSD_seg[-1] = []
for idx in range(len(rnd_test)):
# Object under analysis
oua = SSD_all[idx_exp][idx]
if rnd_test[idx] > segm[-2]:
SSD_seg[-1].append(oua)
SSD_seg_all.append(SSD_seg) # [exp][seg][item]
# MAD
MAD_seg[-1] = []
for idx in range(len(rnd_test)):
# Object under analysis
oua = MAD_all[idx_exp][idx]
if rnd_test[idx] > segm[-2]:
MAD_seg[-1].append(oua)
MAD_seg_all.append(MAD_seg) # [exp][seg][item]
# PRD
PRD_seg[-1] = []
for idx in range(len(rnd_test)):
# Object under analysis
oua = PRD_all[idx_exp][idx]
if rnd_test[idx] > segm[-2]:
PRD_seg[-1].append(oua)
PRD_seg_all.append(PRD_seg) # [exp][seg][item]
# COS SIM
COS_SIM_seg[-1] = []
for idx in range(len(rnd_test)):
# Object under analysis
oua = COS_SIM_all[idx_exp][idx]
if rnd_test[idx] > segm[-2]:
COS_SIM_seg[-1].append(oua)
COS_SIM_seg_all.append(COS_SIM_seg) # [exp][seg][item]
# Printing Tables
seg_table_column_name = []
for idx_seg in range(len(segm) - 1):
column_name = str(segm[idx_seg]) + ' < noise < ' + str(segm[idx_seg + 1])
seg_table_column_name.append(column_name)
# SSD Table
SSD_seg_all = np.array(SSD_seg_all)
SSD_seg_all = np.swapaxes(SSD_seg_all, 0, 1)
print('\n')
print('Printing Table for different noise values on the SSD metric')
vs.generate_table(seg_table_column_name, SSD_seg_all, Exp_names)
# MAD Table
MAD_seg_all = np.array(MAD_seg_all)
MAD_seg_all = np.swapaxes(MAD_seg_all, 0, 1)
print('\n')
print('Printing Table for different noise values on the MAD metric')
vs.generate_table(seg_table_column_name, MAD_seg_all, Exp_names)
# PRD Table
PRD_seg_all = np.array(PRD_seg_all)
PRD_seg_all = np.swapaxes(PRD_seg_all, 0, 1)
print('\n')
print('Printing Table for different noise values on the PRD metric')
vs.generate_table(seg_table_column_name, PRD_seg_all, Exp_names)
# COS SIM Table
COS_SIM_seg_all = np.array(COS_SIM_seg_all)
COS_SIM_seg_all = np.swapaxes(COS_SIM_seg_all, 0, 1)
print('\n')
print('Printing Table for different noise values on the COS SIM metric')
vs.generate_table(seg_table_column_name, COS_SIM_seg_all, Exp_names)
##############################################################################################################
# Metrics graphs
vs.generate_hboxplot(SSD_all, Exp_names, 'SSD (au)', log=False, set_x_axis_size=(0, 100.1))
vs.generate_hboxplot(MAD_all, Exp_names, 'MAD (au)', log=False, set_x_axis_size=(0, 3.01))
vs.generate_hboxplot(PRD_all, Exp_names, 'PRD (au)', log=False, set_x_axis_size=(0, 100.1))
vs.generate_hboxplot(COS_SIM_all, Exp_names, 'Cosine Similarity (0-1)', log=False, set_x_axis_size=(0, 1))
################################################################################################################
# Visualize signals
signals_index = np.array([110, 210, 410, 810, 1610, 3210, 6410, 12810]) + 10
ecg_signals2plot = []
ecgbl_signals2plot = []
dl_signals2plot = []
fil_signals2plot = []
signal_amount = 10
[X_test, y_test, y_pred] = test_Multibranch_LANLD
for id in signals_index:
ecgbl_signals2plot.append(X_test[id])
ecg_signals2plot.append(y_test[id])
dl_signals2plot.append(y_pred[id])
[X_test, y_test, y_filter] = test_IIR
for id in signals_index:
fil_signals2plot.append(y_filter[id])
for i in range(len(signals_index)):
vs.ecg_view(ecg=ecg_signals2plot[i],
ecg_blw=ecgbl_signals2plot[i],
ecg_dl=dl_signals2plot[i],
ecg_f=fil_signals2plot[i],
signal_name=None,
beat_no=None)
vs.ecg_view_diff(ecg=ecg_signals2plot[i],
ecg_blw=ecgbl_signals2plot[i],
ecg_dl=dl_signals2plot[i],
ecg_f=fil_signals2plot[i],
signal_name=None,
beat_no=None)