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Data_Manipulation.py
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Fri, Apr 26, 11:20

Data_Manipulation.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Feb 8 22:10:18 2020
---------------------------------------------------------------------
-- Author: Vigneashwara Pandiyan
---------------------------------------------------------------------
Main file to execute the model on the LPBF dataset
"""
#%%
import torch
from torch.utils.data import Dataset, DataLoader,TensorDataset
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split# implementing train-test-split
#%% Normalize the dataset
def normalize(Features):
Features_1=np.load(Features)
df = pd.DataFrame(Features_1)
df = df.apply(lambda x: (x - np.mean(x))/np.std(x), axis=1)
df = df.to_numpy()
return df
#%% Convert into torch tensor
class Mechanism(Dataset):
def __init__(self,sequences):
self.sequences = sequences
def __len__(self):
return len(self.sequences)
def __getitem__(self,idx):
sequence_1,sequence_2,sequence_3,sequence_4,label = self.sequences [idx]
sequence_1=torch.Tensor(sequence_1)
sequence_2=torch.Tensor(sequence_2)
sequence_3=torch.Tensor(sequence_3)
sequence_4=torch.Tensor(sequence_4)
sequence1 = sequence_1.view(1, -1)
sequence2 = sequence_2.view(1, -1)
sequence3 = sequence_3.view(1, -1)
sequence4 = sequence_4.view(1, -1)
sequence=torch.cat((sequence1, sequence2,sequence3, sequence4), 0)
# print("sequence",sequence.shape)
label=torch.tensor(label).long()
# sequence,label
return sequence,label
#%%
def data_prep(Rawspace_1,Rawspace_2,Rawspace_3,Rawspace_4,classspace):
sequences=[]
for i in range(len(classspace)):
# print(i)
sequence_features_1 = Rawspace_1[i]
sequence_features_2 = Rawspace_2[i]
sequence_features_3 = Rawspace_3[i]
sequence_features_4 = Rawspace_4[i]
label = classspace[i]
sequences.append((sequence_features_1,sequence_features_2,sequence_features_3,sequence_features_4,label))
return sequences
#%% Loading the data for training the model
def dataloading_funtion(folder,window):
rawfile_1 = str(folder)+'/'+'Channel0_'+str(window)+'.npy'
rawfile_2 = str(folder)+'/'+'Channel1_'+str(window)+'.npy'
rawfile_3 = str(folder)+'/'+'Channel2_'+str(window)+'.npy'
rawfile_4 = str(folder)+'/'+'Channel3_'+str(window)+'.npy'
classfile = str(folder)+'/'+'classspace_'+str(window)+'.npy'
Rawspace_1 = normalize(rawfile_1).astype(np.float64)
Rawspace_2 = normalize(rawfile_2).astype(np.float64)
Rawspace_3 = normalize(rawfile_3).astype(np.float64)
Rawspace_4 = normalize(rawfile_4).astype(np.float64)
classspace= np.load(classfile).astype(np.float64)
trainset,testset=data_batch_prep(Rawspace_1,Rawspace_2,Rawspace_3,Rawspace_4,classspace)
return trainset,testset,classspace
#%%
def data_batch_prep(Rawspace_1,Rawspace_2,Rawspace_3,Rawspace_4,classspace):
sequences_batch =[]
for i in range(len(classspace)):
# print(i)
sequence_features_1 = Rawspace_1[i]
# sequence_features_1 = normalize(sequence_features_1)
sequence_features_2 = Rawspace_2[i]
# sequence_features_2 = normalize(sequence_features_2)
sequence_features_3 = Rawspace_3[i]
# sequence_features_3 = normalize(sequence_features_3)
sequence_features_4 = Rawspace_4[i]
# sequence_features_4 = normalize(sequence_features_4)
label = classspace[i]
sequences_batch.append((sequence_features_1,sequence_features_2,sequence_features_3,sequence_features_4,label))
sequences_batch = Mechanism(sequences_batch)
train, test = train_test_split(sequences_batch, test_size=0.3,random_state=42)
trainset = torch.utils.data.DataLoader(train, batch_size=200, num_workers=0,
shuffle=True)
testset = torch.utils.data.DataLoader(test, batch_size=200, num_workers=0,
shuffle=True)
return trainset, testset

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