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ML_ECMO/get_data.py
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get_data.py
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import tensorflow as tf
import numpy as np
import RNN as frw
from matplotlib import pyplot as plt
tf.compat.v1.enable_eager_execution()
path = "data_CHUV"
param = ['time', 'ECMO_Débit', 'ECMO_FmO2%', 'ECMO_Tours / min', 'ECMO_type', 'FC', 'FIO2%', 'Glasgow_DM num', 'Lac_art', 'ModeVentMec', 'M-Pplateau', 'M-VentMin', 'M-VT', 'NPi D Pupillomètre', 'NPi G Pupillomètre', 'P. Veineuse_ECMO', 'P.IN_ECMO', 'P.OUT_ECMO', 'PetCO2', 'Pupille D Pupillomètre', 'Pupille G Pupillomètre', 'PupilleD', 'PupilleG', 'PVC/OD', 'RC', 'R-I:Eratio', 'R-PEEP', 'SO2_art', 'SpO2', 'SvO2_ECMO', 'TAD', 'TAM', 'TAS']
times = ['42893.81944', '42893.82778', '42893.82986', '42893.83333', '42893.84444', '42893.84514', '42893.84583', '42893.84722', '42893.84792', '42893.84931', '42893.85', '42893.85208', '42893.85278', '42893.85347', '42893.85486', '42893.85764', '42893.85903', '42893.85972', '42893.86181', '42893.86458', '42893.86597', '42893.86667', '42893.86875', '42893.87083', '42893.87292', '42893.87361', '42893.875', '42893.87569', '42893.87986', '42893.88056', '42893.88264', '42893.88681', '42893.8875', '42893.88958', '42893.89167', '42893.89375', '42893.89444', '42893.89653', '42893.89861', '42893.9', '42893.90069', '42893.90139', '42893.90347', '42893.90556', '42893.90764', '42893.90833', '42893.91042', '42893.9125', '42893.91458', '42893.91528', '42893.91667', '42893.91736', '42893.91944', '42893.92153', '42893.92222', '42893.92431', '42893.92639', '42893.92847', '42893.92917', '42893.93125', '42893.93333', '42893.93542', '42893.93611', '42893.93819', '42893.94028', '42893.94236', '42893.94306', '42893.94514', '42893.94722', '42893.94931', '42893.95', '42893.95208', '42893.95417', '42893.95625', '42893.95694', '42893.95833', '42893.95903', '42893.96111', '42893.96319', '42893.96389', '42893.96597', '42893.96806', '42893.97014', '42893.97083', '42893.97153', '42893.97292', '42893.97639', '42893.97708', '42893.97778', '42893.97986', '42893.98194', '42893.98403', '42893.98472', '42893.98681', '42893.98819', '42893.98889', '42893.99097', '42893.99167', '42893.99375', '42893.99583', '42893.99792', '42893.99861', '42893.99931', '42894', '42894.00069', '42894.00208', '42894.00278', '42894.00486', '42894.00556', '42894.00764', '42894.00903', '42894.00972', '42894.01181', '42894.0125', '42894.01458', '42894.01667', '42894.01875', '42894.01944', '42894.02014', '42894.02153', '42894.02361', '42894.02569', '42894.02639', '42894.02847', '42894.03125', '42894.03264', '42894.03333', '42894.03542', '42894.0375', '42894.03889', '42894.03958', '42894.04028', '42894.04167', 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'42896.19236', '42896.19375', '42896.19444', '42896.19514', '42896.19583', '42896.19653', '42896.19792', '42896.19861', '42896.19931', '42896.20069', '42896.20139', '42896.20208', '42896.20278', '42896.20347', '42896.20486', '42896.20556', '42896.20625', '42896.20764', '42896.20833', '42896.20903', '42896.20972', '42896.21042', '42896.21181', '42896.2125', '42896.21319', '42896.21458', '42896.21528', '42896.21597', '42896.21667', '42896.21736', '42896.21875', '42896.21944', '42896.22014', '42896.22153', '42896.22222', '42896.22292', '42896.22361', '42896.22431', '42896.22569', '42896.22639', '42896.22708', '42896.22847', '42896.22917', '42896.22986', '42896.23056', '42896.23125', '42896.23264', '42896.23333', '42896.23403', '42896.23542', '42896.23611', '42896.23681', '42896.2375', '42896.23819', '42896.23958', '42896.24028', '42896.24097', '42896.24236', '42896.24306', '42896.24375', '42896.24444', '42896.24514', '42896.24653', '42896.24722', '42896.24792', '42896.24931', '42896.25', '42896.25069', '42896.25139', '42896.25208', '42896.25347', '42896.25417', '42896.25486', '42896.25694', '42896.25764', '42896.25833', '42896.25903', '42896.26042', '42896.26111', '42896.26181', '42896.2625', '42896.26319', '42896.26389', '42896.26458', '42896.26528', '42896.26597', '42896.26736', '42896.26806', '42896.26875', '42896.27014', '42896.27083', '42896.27153', '42896.27222', '42896.27292', '42896.27431', '42896.275', '42896.27569', '42896.27708', '42896.27778', '42896.27847', '42896.27917', '42896.27986', '42896.28125', '42896.28194', '42896.28264', '42896.28472', '42896.28542', '42896.28611', '42896.28681', '42896.28819', '42896.28889', '42896.28958', '42896.29097', '42896.29167', '42896.29236', '42896.29306', '42896.29375', '42896.29514', '42896.29583', '42896.29653', '42896.29792', '42896.29861', '42896.29931', '42896.3', '42896.30069', '42896.30208', '42896.30278', '42896.30347', '42896.30486', '42896.30556', '42896.30625', '42896.30694', '42896.30764', '42896.30903', '42896.30972', '42896.31042', '42896.31181', '42896.3125', '42896.31319', '42896.31389', '42896.31458', '42896.31597', '42896.31667', '42896.31736', '42896.31875', '42896.31944', '42896.32014', '42896.32083', '42896.32153', '42896.32292', '42896.32361', '42896.32431', '42896.32569', '42896.32639', '42896.32708', '42896.32778', '42896.32847', '42896.32986', '42896.33056', '42896.33125', '42896.33264', '42896.33333', '42896.33403', '42896.33472', '42896.33542', '42896.33681', '42896.3375', '42896.33819', '42896.33958', '42896.34028', '42896.34097', '42896.34167', '42896.34236', '42896.34375', '42896.34444', '42896.34514', '42896.34653', '42896.34722', '42896.34792', '42896.34861', '42896.34931', '42896.35069', '42896.35139', '42896.35208', '42896.35417', '42896.35486', '42896.35556', '42896.35625', '42896.35764', '42896.35833', '42896.35903', '42896.36042', '42896.36111', '42896.36181', '42896.3625', '42896.36319', '42896.36458', '42896.36597', '42896.36736', '42896.36806', '42896.36875', '42896.36944', '42896.37014', '42896.37153', '42896.37222', '42896.37292', '42896.37431', '42896.375', '42896.37569', '42896.37639', '42896.37708', '42896.37847', '42896.37917', '42896.37986', '42896.38125', '42896.38194', '42896.38264', '42896.38333', '42896.38403', '42896.38542', '42896.38611', '42896.38681', '42896.38819', '42896.38889', '42896.38958', '42896.39028', '42896.39097', '42896.39236', '42896.39306', '42896.39375', '42896.39583', '42896.39653', '42896.39722', '42896.39792', '42896.39931', '42896.4', '42896.40069', '42896.40208', '42896.40278', '42896.40347', '42896.40417', '42896.40486', '42896.40625', '42896.40694', '42896.40764', '42896.40903', '42896.40972', '42896.41042', '42896.41111', '42896.41181', '42896.41319', '42896.41389', '42896.41458', '42896.41597', '42896.41667', '42896.41736', '42896.41806', '42896.41875', '42896.42014', '42896.42083', '42896.42153', '42896.42292', '42896.42361', '42896.42431', '42896.425', '42896.42569', '42896.42708', '42896.42778', '42896.42847', '42896.42986', '42896.43472', '42896.43611', '42896.43819', '42896.43889', '42896.44097', '42896.44167', '42896.44236', '42896.44514', '42896.44583', '42896.44792', '42896.44861', '42896.44931', '42896.45208', '42896.45278', '42896.45486', '42896.45556', '42896.45625', '42896.45833', '42896.45903', '42896.45972', '42896.46181', '42896.4625', '42896.46319', '42896.46597', '42896.46667', '42896.46875', '42896.46944', '42896.47014', '42896.47292', '42896.47361', '42896.47569', '42896.47639', '42896.47708', '42896.47986', '42896.48056', '42896.48264', '42896.48333', '42896.48403', '42896.48681', '42896.4875', '42896.48958', '42896.49028', '42896.49097', '42896.49375', '42896.49444', '42896.49653', '42896.49722', '42896.49792', '42896.5', '42896.50069', '42896.50139', '42896.50347', '42896.50417', '42896.50486', '42896.50556', '42896.50694', '42896.50764', '42896.50833', '42896.50972', '42896.51042', '42896.51111', '42896.51181', '42896.51389', '42896.51458', '42896.51528', '42896.51667', '42896.51736', '42896.51806', '42896.51875', '42896.52083', '42896.52153', '42896.52222', '42896.52361', '42896.52431', '42896.525', '42896.52569', '42896.52778', '42896.52847', '42896.52917', '42896.53056', '42896.53125', '42896.53194', '42896.53264', '42896.53472', '42896.53542', '42896.53611', '42896.5375', '42896.53819', '42896.53889', '42896.53958', '42896.54167', '42896.54236', '42896.54306', '42896.54514', '42896.54583', '42896.54653', '42896.54861', '42896.54931', '42896.55', '42896.55139', '42896.55208', '42896.55278', '42896.55347', '42896.55556', '42896.55625', '42896.55694', '42896.55833', '42896.55903', '42896.55972', '42896.56042', '42896.5625', '42896.56319', '42896.56389', '42896.56528', '42896.56597', '42896.56667', '42896.56736', '42896.56944', '42896.57014', '42896.57083', '42896.57222', '42896.57292', '42896.57361', '42896.57431', '42896.57639', '42896.57708', '42896.57778', '42896.57917', '42896.57986', '42896.58056', '42896.58125', '42896.58333', '42896.58403', '42896.58472', '42896.58611', '42896.58681', '42896.5875', '42896.58819', '42896.59028', '42896.59097', '42896.59167', '42896.59306', '42896.59375', '42896.59444', '42896.59514', '42896.59722', '42896.59792', '42896.59861', '42896.6', '42896.60069', '42896.60139', '42896.60208', '42896.60417', '42896.60486', '42896.60556', '42896.60694', '42896.60764', '42896.60833', '42896.60903', '42896.61111', '42896.61181', '42896.6125', '42896.61389', '42896.61458', '42896.61528', '42896.61597', '42896.61806', '42896.61875', '42896.61944', '42896.62083', '42896.62153', '42896.62222', '42896.62292', '42896.625', '42896.62569', '42896.62639', '42896.62778', '42896.62847', '42896.62917', '42896.62986', '42896.63056', '42896.63125', '42896.63194', '42896.63264', '42896.63333', '42896.63403', '42896.63472', '42896.63542', '42896.63611', '42896.63681', '42896.6375', '42896.63819', '42896.63889', '42896.63958', '42896.64028', '42896.64097', '42896.64167', '42896.64236', '42896.64306', '42896.64375', '42896.64444', '42896.64514', '42896.64583', '42896.64653', '42896.64722', '42896.64792', '42896.64861', '42896.64931', '42896.65', '42896.65069', '42896.65139', '42896.65208', '42896.71042', '42896.74097', '42896.79375', '42902.43056']
nb_epochs, batch_size, nb_samples = 75, 100, 10
dim_input, dim_recurrent, dim_output = 3, 32, 3
learning_rate = 1e-3
time_steps = 31
offset = 5
input_train_arrays, target_train_arrays = [], []
input_test_arrays, target_test_arrays = [], []
train_loss_tab = []
data_array = []
for i in range(nb_samples):
data_array.append(np.load(path + '/data/tab_' + str(i) + '.npy', allow_pickle=True))
INPUT_TRAIN_FILE_NAMES = ['files_' + str(dim_input) + '/train/input/file_' + str(i) + '.txt' for i in range(nb_samples)]
TARGET_TRAIN_FILE_NAMES = ['files_' + str(dim_input) + '/train/target/file_' + str(i) + '.txt' for i in range(nb_samples)]
INPUT_TEST_FILE_NAMES = ['files_' + str(dim_input) + '/test/input/file_' + str(i) + '.txt' for i in range(nb_samples)]
TARGET_TEST_FILE_NAMES = ['files_' + str(dim_input) + '/test/target/file_' + str(i) + '.txt' for i in range(nb_samples)]
# Stack the different samples
train_input = tf.stack([input_train_arrays[i] for i in range(nb_samples)])
train_target = tf.stack([target_train_arrays[i] for i in range(nb_samples)])
test_input = tf.stack([input_test_arrays[i] for i in range(nb_samples)])
test_target = tf.stack([target_test_arrays[i] for i in range(nb_samples)])
# Transpose the second and the third dimension in order to have (samples, time, parameters)
train_input = tf.transpose(train_input, perm=[0, 2, 1])
train_target = tf.transpose(train_target, perm=[0, 2, 1])
test_input = tf.transpose(test_input, perm=[0, 2, 1])
test_target = tf.transpose(test_target, perm=[0, 2, 1])
################################################################################################
# NORMALIZATION
# Get the min and the max along the first dimension (samples)
max_input = tf.reduce_max(train_input, reduction_indices=[0])
min_input = tf.reduce_min(train_input, reduction_indices=[0])
max_target = tf.reduce_max(train_target, reduction_indices=[0])
min_target = tf.reduce_min(train_target, reduction_indices=[0])
"""
max_input = tf.reduce_max(max_input, reduction_indices=[0])
min_input = tf.reduce_min(min_input, reduction_indices=[0])
max_target = tf.reduce_max(max_target, reduction_indices=[0])
min_target = tf.reduce_min(min_target, reduction_indices=[0])
"""
"""
max_input = tf.reduce_max(train_input, reduction_indices=[1])
max_input = tf.reshape(max_input, shape=[nb_samples,1,dim_input])
min_input = tf.reduce_min(train_input, reduction_indices=[1])
min_input = tf.reshape(min_input, shape=[nb_samples,1,dim_input])
max_target = tf.reduce_max(train_target, reduction_indices=[1])
max_target = tf.reshape(max_target, shape=[nb_samples,1,dim_input])
min_target = tf.reduce_min(train_target, reduction_indices=[1])
min_target = tf.reshape(min_target, shape=[nb_samples,1,dim_input])
"""
# Apply the normalization
train_input = tf.divide(train_input - min_input, max_input - min_input)
test_input = tf.divide(test_input - min_input, max_input - min_input)
train_target = tf.divide(train_target - min_target, max_target - min_target)
test_target = tf.divide(test_target - min_target, max_target - min_target)
################################################################################################
# TRAINING WITH ZEROS INITIAL STATE
states = list(tf.zeros(shape=[dim_recurrent]) for i in range(nb_samples))
model = frw.RNN(dim_input, dim_recurrent, dim_output)
#model = frw.GatedRNNComplex(dim_input, dim_recurrent, dim_output)
#model = frw.RNN2Cells(dim_input, dim_recurrent, dim_output)
#frw.train_model_2cells(model, train_input, train_target, states, learning_rate, num_epochs=nb_epochs,
# mini_batch_size=batch_size, loss_tab=train_loss_tab)
frw.train_model(model, train_input, train_target, states, learning_rate, num_epochs=nb_epochs,
mini_batch_size=batch_size, loss_tab=train_loss_tab)
################################################################################################
# TRAINING WITH STATES = INPUTS AT TIME 0
"""
states = list(train_input[i, 0, :] for i in range(nb_samples))
model = frw.RNN2(dim_input, dim_recurrent, dim_output)
#model = frw.GatedRNN(dim_input, dim_recurrent, dim_output)
frw.train_model(model, train_input, train_target, states, learning_rate, num_epochs=nb_epochs,
mini_batch_size=batch_size, loss_tab=train_loss_tab)
states = list(test_input[i, 0, :] for i in range(nb_samples))
"""
################################################################################################
# TRAINING WITH STATES = TARGETS AT TIME 0
"""
states = list(train_target[i, 0, :] for i in range(nb_samples))
model = frw.RNN1(dim_input, dim_recurrent, dim_output)
frw.train_model(model, train_input, train_target, states, learning_rate, num_epochs=nb_epochs,
mini_batch_size=batch_size, loss_tab=train_loss_tab)
states = list(test_target[i, 0, :] for i in range(nb_samples))
"""
################################################################################################
# TEST
predictions = [test_target[:, 0, :]]
for t in range(time_steps-1):
#input_data = [test_input[:, i, :] for i in range(t, t+2)]
#prediction, states = model(input_data, states)
prediction, states = model(test_input[:, t+1, :], states)
predictions.append(prediction)
predictions = tf.stack(predictions, axis=1)
################################################################################################
# PLOT
predictions = tf.multiply(predictions, max_target - min_target) + min_target
test_target = tf.multiply(test_target, max_target - min_target) + min_target
test_input = tf.multiply(test_input, max_input - min_input) + min_input
t = [(5/(time_steps-1))*i for i in range(time_steps)]
plt.plot(t, test_target[0, :, 2], color='green', label='target')
plt.plot(t, predictions[0, :, 2], color='red', label='prediction')
plt.plot(t, test_input[0, :, 2], color='blue', label='input')
plt.legend()
plt.show()
axis = [i for i in range(nb_epochs * train_input.shape[0]//batch_size)]
plt.semilogy(axis, train_loss_tab, color='black')
plt.show()
"""
#print(data1[0:5])
times = np.load('times.npy', allow_pickle=True)
params = np.load('params.npy', allow_pickle=True)
print(params[0])
max_params = params[0].copy()
for list in params:
for param in list:
if param not in max_params:
max_params.append(param)
#max_params[1:].sort()
max_params.sort()
max_params.remove('time')
max_params.insert(0, 'time')
print(max_params)
print(len(max_params))
#dataset = tf.data.Dataset.from_tensor_slices([data1, data2])
for i in range(1):
df = pd.read_csv(path + '/data/Signals_' + str(i) + '.csv', delimiter=';')
print(df.head(6))
missing_params = [pd.DataFrame({param: [float('NaN')]*len(times[i])}) for param in max_params
if param not in params[i]]
#print([param for param in max_params if param not in params[i]])
for j in range(len(missing_params)):
#df = df.reset_index(drop=True)
#missing_params[j] = missing_params[j].reset_index(drop=True)
df = pd.concat([df, missing_params[j]], axis=1)
#df.append(missing_params[j])
print('max_params : ', max_params)
print(df.head(6))
df.sort_values(max_params, axis=1, inplace=True)
"""
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