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sample_creator_unit_auto.py
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sample_creator_unit_auto.py
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'''
DL models (FNN, 1D CNN and CNN-LSTM) evaluation on N-CMAPSS
12.07.2021
Hyunho Mo
hyunho.mo@unitn.it
'''
## Import libraries in python
import gc
import argparse
import os
import json
import logging
import sys
import h5py
import time
import matplotlib
import numpy as np
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
from matplotlib import gridspec
import math
import random
import importlib
from scipy.stats import randint, expon, uniform
import sklearn as sk
from sklearn import svm
from sklearn.utils import shuffle
from sklearn import metrics
from sklearn import preprocessing
from sklearn import pipeline
# from sklearn.metrics import mean_squared_error
from math import sqrt
import scipy.stats as stats
from utils.data_preparation_unit import df_all_creator, df_train_creator, df_test_creator, Input_Gen
seed = 0
random.seed(0)
np.random.seed(seed)
current_dir = os.path.dirname(os.path.abspath(__file__))
data_filedir = os.path.join(current_dir, 'N-CMAPSS')
data_filepath = os.path.join(current_dir, 'N-CMAPSS', 'N-CMAPSS_DS02-006.h5')
def main():
# current_dir = os.path.dirname(os.path.abspath(__file__))
parser = argparse.ArgumentParser(description='sample creator')
parser.add_argument('-w', type=int, default=10, help='window length', required=True)
parser.add_argument('-s', type=int, default=10, help='stride of window')
parser.add_argument('--sampling', type=int, default=1, help='sub sampling of the given data. If it is 10, then this indicates that we assumes 0.1Hz of data collection')
parser.add_argument('--test', type=int, default='non', help='select train or test, if it is zero, then extract samples from the engines used for training')
args = parser.parse_args()
sequence_length = args.w
stride = args.s
sampling = args.sampling
selector = args.test
# Load data
'''
W: operative conditions (Scenario descriptors)
X_s: measured signals
X_v: virtual sensors
T(theta): engine health parameters
Y: RUL [in cycles]
A: auxiliary data
'''
df_all = df_all_creator(data_filepath, sampling)
'''
Split dataframe into Train and Test
Training units: 2, 5, 10, 16, 18, 20
Test units: 11, 14, 15
'''
# units = list(np.unique(df_A['unit']))
units_index_train = [2.0, 5.0, 10.0, 16.0, 18.0, 20.0]
units_index_test = [11.0, 14.0, 15.0]
print("units_index_train", units_index_train)
print("units_index_test", units_index_test)
# if any(int(idx) == unit_index for idx in units_index_train):
# df_train = df_train_creator(df_all, units_index_train)
# print(df_train)
# print(df_train.columns)
# print("num of inputs: ", len(df_train.columns) )
# df_test = pd.DataFrame()
#
# else :
# df_test = df_test_creator(df_all, units_index_test)
# print(df_test)
# print(df_test.columns)
# print("num of inputs: ", len(df_test.columns))
# df_train = pd.DataFrame()
df_train = df_train_creator(df_all, units_index_train)
print(df_train)
print(df_train.columns)
print("num of inputs: ", len(df_train.columns) )
df_test = df_test_creator(df_all, units_index_test)
print(df_test)
print(df_test.columns)
print("num of inputs: ", len(df_test.columns))
del df_all
gc.collect()
df_all = pd.DataFrame()
sample_dir_path = os.path.join(data_filedir, 'Samples_whole')
sample_folder = os.path.isdir(sample_dir_path)
if not sample_folder:
os.makedirs(sample_dir_path)
print("created folder : ", sample_dir_path)
cols_normalize = df_train.columns.difference(['RUL', 'unit'])
sequence_cols = df_train.columns.difference(['RUL', 'unit'])
if selector == 0:
for unit_index in units_index_train:
data_class = Input_Gen (df_train, df_test, cols_normalize, sequence_length, sequence_cols, sample_dir_path,
unit_index, sampling, stride =stride)
data_class.seq_gen()
else:
for unit_index in units_index_test:
data_class = Input_Gen (df_train, df_test, cols_normalize, sequence_length, sequence_cols, sample_dir_path,
unit_index, sampling, stride =stride)
data_class.seq_gen()
if __name__ == '__main__':
main()