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sample_creator_unit.py
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sample_creator_unit.py
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'''
DL models (FNN, 1D CNN and CNN-LSTM) evaluation on N-CMAPSS
'''
# Import libraries in python
from utils.data_preparation_unit import df_all_creator, df_train_creator,\
df_test_creator, Input_Gen
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
# import seaborn as sns
# 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
# from tqdm import tqdm
# import scipy.stats as stats
# from sklearn.utils.testing import ignore_warnings
# from sklearn.exceptions import ConvergenceWarning
# import keras
import tensorflow as tf
print(tf.__version__)
# import keras.backend as K
# import tensorflow.keras.backend as K
# from tensorflow.keras import backend
# from tensorflow.keras import optimizers
# from tensorflow.keras.models import Sequential, load_model, Model
# from tensorflow.keras.layers import Input, Dense, Flatten, Dropout, Embedding
# from tensorflow.keras.layers import BatchNormalization, Activation, LSTM, \
# TimeDistributed, Bidirectional
# from tensorflow.keras.layers import Conv1D
# from tensorflow.keras.layers import MaxPooling1D
# from tensorflow.keras.layers import concatenate
# from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
# import tensorflow.compat.v1 as tf
# tf.disable_v2_behavior()
seed = 0
random.seed(0)
np.random.seed(seed)
# Ignore tf err log
pd.options.mode.chained_assignment = None # default='warn'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
tf.get_logger().setLevel(logging.ERROR)
current_dir = os.path.dirname(os.path.abspath(__file__))
data_filedir = os.path.join(current_dir, 'Data/N-CMAPSS')
data_filepath = os.path.join(current_dir, 'Data/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='Input sources', required=True)
parser.add_argument('-s', type=int, default=10, help='sequence length')
parser.add_argument('--index', type=int, default='non',
help='data representation:non, sfa or pca')
args = parser.parse_args()
sequence_length = args.w
stride = args.s
unit_index = args.index
# 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)
'''
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')
cols_normalize = df_train.columns.difference(['RUL', 'unit'])
sequence_cols = df_train.columns.difference(['RUL', 'unit'])
data_class = Input_Gen(df_train, df_test, cols_normalize,
sequence_length, sequence_cols, sample_dir_path,
unit_index, stride=stride)
data_class.seq_gen()
if __name__ == '__main__':
main()