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data_processing.py
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import numpy as np
from sklearn import preprocessing
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import matplotlib.pyplot as plt
import pandas as pd
from scipy import interpolate
import torch
scaler = preprocessing.MinMaxScaler()
def process_data(data_dir, data_identifier,winSize=30):
RUL_01 = np.loadtxt(data_dir + 'RUL_' + data_identifier +'.txt')
train_01_raw = pd.read_csv(data_dir + '/train_'+data_identifier+'.txt', sep=" ", header=None)
train_01_raw.drop(train_01_raw.columns[[26, 27]], axis=1, inplace=True)
train_01_raw.columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',
's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',
's15', 's16', 's17', 's18', 's19', 's20', 's21']
train_01_raw = train_01_raw.sort_values(['id','cycle'])
test_01_raw = pd.read_csv(data_dir + '/test_'+data_identifier+'.txt', sep=" ", header=None)
test_01_raw.drop(test_01_raw.columns[[26, 27]], axis=1, inplace=True)
test_01_raw.columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',
's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',
's15', 's16', 's17', 's18', 's19', 's20', 's21']
test_01_raw = test_01_raw.sort_values(['id','cycle'])
if data_identifier == 'FD002' or data_identifier == 'FD004':
max_RUL = 130
print("--multi operating conditions--")
train_01_raw.loc[train_01_raw['setting1'].between(0.00000e+00, 3.00000e-03), 'setting1'] = 0.0
train_01_raw.loc[train_01_raw['setting1'].between(9.99800e+00, 1.00080e+01), 'setting1'] = 10.0
train_01_raw.loc[train_01_raw['setting1'].between(1.99980e+01, 2.00080e+01), 'setting1'] = 20.0
train_01_raw.loc[train_01_raw['setting1'].between(2.49980e+01, 2.50080e+01), 'setting1'] = 25.0
train_01_raw.loc[train_01_raw['setting1'].between(3.49980e+01, 3.50080e+01), 'setting1'] = 35.0
train_01_raw.loc[train_01_raw['setting1'].between(4.19980e+01, 4.20080e+01), 'setting1'] = 42.0
test_01_raw.loc[test_01_raw['setting1'].between(0.00000e+00, 3.00000e-03), 'setting1'] = 0.0
test_01_raw.loc[test_01_raw['setting1'].between(9.99800e+00, 1.00080e+01), 'setting1'] = 10.0
test_01_raw.loc[test_01_raw['setting1'].between(1.99980e+01, 2.00080e+01), 'setting1'] = 20.0
test_01_raw.loc[test_01_raw['setting1'].between(2.49980e+01, 2.50080e+01), 'setting1'] = 25.0
test_01_raw.loc[test_01_raw['setting1'].between(3.49980e+01, 3.50080e+01), 'setting1'] = 35.0
test_01_raw.loc[test_01_raw['setting1'].between(4.19980e+01, 4.20080e+01), 'setting1'] = 42.0
train_sensor = train_01_raw.iloc[:, 2:]
test_sensor = test_01_raw.iloc[:, 2:]
Train_Norm = pd.DataFrame(columns = train_sensor.columns[3:])
Test_Norm = pd.DataFrame(columns = test_sensor.columns[3:])
grouped_train = train_sensor.groupby('setting1')
grouped_test = test_sensor.groupby('setting1')
for train_idx, train in grouped_train:
scaled_train = scaler.fit_transform(train.iloc[:, 3:])
scaled_train_combine = pd.DataFrame(
data=scaled_train,
index= train.index,
columns=train_sensor.columns[3:])
Train_Norm = pd.concat([Train_Norm, scaled_train_combine])
for test_idx, test in grouped_test:
if train_idx == test_idx:
scaled_test = scaler.transform(test.iloc[:, 3:])
scaled_test_combine = pd.DataFrame(
data=scaled_test,
index= test.index,
columns=test_sensor.columns[3:])
Test_Norm = pd.concat([Test_Norm, scaled_test_combine])
Train_Norm = Train_Norm.sort_index()
Test_Norm = Test_Norm.sort_index()
train_01_raw.iloc[:, 2:5] = scaler.fit_transform(train_01_raw.iloc[:, 2:5])
test_01_raw.iloc[:, 2:5] = scaler.transform(test_01_raw.iloc[:, 2:5])
Train_Settings = pd.DataFrame(
data=train_01_raw.iloc[:, :5],
index= train_01_raw.index,
columns=train_01_raw.columns[:5])
Test_Settings = pd.DataFrame(
data=test_01_raw.iloc[:, :5],
index= test_01_raw.index,
columns=test_01_raw.columns[:5])
#adding the column of 'time'
train_01_nor = pd.concat([Train_Settings, Train_Norm], axis = 1)
test_01_nor = pd.concat([Test_Settings, Test_Norm], axis = 1)
train_01_nor = train_01_nor.values
test_01_nor = test_01_nor.values
train_01_nor = np.delete(train_01_nor, [5,6,9,10,11,12,14,16,17,20,22,23], axis=1) # sensor 1 for index 5 2,3,4,
test_01_nor = np.delete(test_01_nor, [5,6,9,10,11,12,14,16,17,20,22,23], axis=1)
else:
print("--single operating conditions--")
max_RUL = 125.0
with np.nditer(train_01_raw['setting1'], op_flags=['readwrite']) as it:
for x in it:
x[...] = 0.0
#skip the first 2 columns, id and cycle
train_01_raw.iloc[:, 2:] = scaler.fit_transform(train_01_raw.iloc[:, 2:])
test_01_raw.iloc[:, 2:] = scaler.transform(test_01_raw.iloc[:, 2:])
train_01_nor = train_01_raw
test_01_nor = test_01_raw
train_01_nor = train_01_nor.values
test_01_nor = test_01_nor.values
train_01_nor = np.delete(train_01_nor, [5,9,10,14,20,22,23], axis=1) # sensor 1 for index 5 2,3,4
test_01_nor = np.delete(test_01_nor, [5,9,10,14,20,22,23], axis=1)
train_data, train_labels, train_next_seq, valid_data, valid_labels, valid_next_seq = get_train_valid(train_01_nor,winSize,max_RUL)
testX = []; testY = []; testLen = []
for i in range(1,int(np.max(test_01_nor[:,0]))+1):
ind =np.where(test_01_nor[:,0]==i)
ind = ind[0]
testLen.append(len(ind))
data_temp = test_01_nor[ind,:]
if len(data_temp)<winSize:
data_temp_a = []
for myi in range(data_temp.shape[1]):
x1 = np.linspace(0, winSize-1, len(data_temp) )
x_new = np.linspace(0, winSize-1, winSize)
tck = interpolate.splrep(x1, data_temp[:,myi])
a = interpolate.splev(x_new, tck)
data_temp_a.append(a.tolist())
data_temp_a = np.array(data_temp_a)
data_temp = data_temp_a.T
data_temp = data_temp[:,1:]
else:
data_temp = data_temp[-winSize:,1:]
# print('np.shape(data_temp)', np.shape(data_temp))
# print('data_temp[:,1]', data_temp[:,0])
# break
data_temp = np.reshape(data_temp,(1,data_temp.shape[0],data_temp.shape[1]))
if i == 1:
testX = data_temp
else:
testX = np.concatenate((testX,data_temp),axis = 0)
if RUL_01[i-1] > max_RUL:
testY.append(max_RUL)
else:
testY.append(RUL_01[i-1])
testX = np.array(testX)
train_data[:, :, 0] = scaler.fit_transform(train_data[:, :, 0]) #normalize
valid_data[:, :, 0] = scaler.fit_transform(valid_data[:, :, 0])#normalize
# train_labels = np.array(train_labels)/max_RUL # normalize to 0-1
train_data = train_data[:, :, 4:]# remove wokring setting parameters.
valid_data = valid_data[:, :, 4:]# remove working setting parameters.
# train_labels = np.array(train_labels)/max_RUL # normalize to 0-1
testX[:, :, 0] = scaler.transform(testX[:, :, 0])
testX = testX[:, :, 4:]
# return trainX, testX, trainY, testY#, trainAux, testAux
# train_data = np.delete(train_data, train_data.shape[0] - 1, axis = 0)
# valid_data = np.delete(valid_data, valid_data.shape[0] - 1, axis = 0)
# testX = np.delete(testX, testX.shape[0] - 1, axis = 0)
# train_labels = np.delete(train_labels, train_labels.shape[0] - 1, axis = 0)
# valid_labels = np.delete(valid_labels, valid_labels.shape[0] - 1, axis = 0)
# testY = np.delete(testY, testY.shape[0] - 1, axis = 0)
return train_data, valid_data, testX, train_labels, valid_labels, testY, train_next_seq
#=================================END OF DATA PROCESSING ==============================================
def get_train_valid(data,window_size,max_RUL):
num_engines=int(np.max(data[:,0]))
train_size = int(0.9 *num_engines)
test_size = num_engines - train_size
train_idx, valid_idx = torch.utils.data.random_split(np.arange(num_engines), [train_size, test_size])
train_data,train_labels, train_next_seq = split_data(train_idx.indices,window_size,data,max_RUL)
valid_data,valid_labels, valid_next_seq = split_data(valid_idx.indices,window_size,data,max_RUL)
return train_data,train_labels,train_next_seq,valid_data,valid_labels,valid_next_seq
# def split_data(idx,window_size,data,max_RUL):
# trainX = [];trainY = []
# winSize = window_size
# for i in (idx):
# #the data of the the i_th engine
# ind =np.where(data[:,0]==i)
# #the id of the ith engine
# ind = ind[0]
# data_temp = data[ind,:]
# for j in range(len(data_temp)-winSize+1):
# trainX.append(data_temp[j:j+winSize,1:].tolist())
# train_RUL = len(data_temp)-winSize-j
# if train_RUL > max_RUL:
# train_RUL = max_RUL
# trainY.append(train_RUL)
# trainX = np.array(trainX)
# trainY = np.array(trainY)/max_RUL
# return trainX,trainY
def split_data(idx,window_size,data,max_RUL):
trainX = []
trainY = []
next_seq = []
winSize = window_size
for i in (idx):
#the data of the the i_th engine
ind =np.where(data[:,0]==i)
#the id of the ith engine
ind = ind[0]
data_temp = data[ind,:]
for j in range(len(data_temp)-winSize):
trainX.append(data_temp[j:j+winSize,1:].tolist())
next_seq.append(data_temp[j + 1 : j + winSize + 1, 1 :].tolist())
train_RUL = len(data_temp)-winSize-j
if train_RUL > max_RUL:
train_RUL = max_RUL
trainY.append(train_RUL)
trainX = np.array(trainX)
trainY = np.array(trainY)/max_RUL
next_seq = np.array(next_seq)
return trainX, trainY, next_seq
# data_dir = "E:/RUL/CMAPSSData/"
# train_data, valid_data, testX, train_labels, valid_labels, testY = process_data(data_dir, "FD002")
# data = {'train_data' : train_data,
# 'valid_data' : valid_data,
# 'testX' : testX,
# 'train_labels' : train_labels,
# 'valid_labels' : valid_labels,
# 'testY' : testY
# }
# np.save('data.npy', data)
# read_dictionary = np.load('data.npy',allow_pickle='TRUE').item()
# a = read_dictionary['train_data']
# b = read_dictionary['train_labels']
# print(a.shape)
# print(b.shape)