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main.py
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from install_req import install_missing_pkg
check_pkg = install_missing_pkg() # faz a instalação de pacotes faltantes, se houver
import numpy as np
import copy
import pandas as pd
from tensorflow import keras
from ST_modules.Variational_Autoencoder import VAE
from sklearn.metrics import accuracy_score
from ST_modules.Algorithms import alghms
from ST_modules.Plot_graphs import ST_graphics
from utils import ST_functions
from statistics import mean
import skimage.io
from skimage.io import imread_collection
import os
from sklearn.metrics import precision_recall_fscore_support
import time
import argparse
from ST_modules.ncm import NCM_classifier
from ST_modules.deep_nno import DeepNNO
import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.utils.data import DataLoader
import torch.optim as optim
from ST_modules.resnet import ResNet18
from ST_modules.trainer import Trainer
from ST_modules.data_loader import RODFolder
from sklearn.metrics import average_precision_score
from sklearn.preprocessing import label_binarize
def load_dataset(dataset_name, vae, vae_epoch, lat_dim, len_train):
script_dir = os.path.dirname(__file__)
# inserir os outros datasets aqui
if dataset_name == 'nno_ceratocystis1' and vae == False:
dir_path = 'data/train_images/dataset_eucapytus/dataset-1/*.png'
images_path = 'data/train_images/dataset_eucapytus/dataset-1'
files = os.listdir(images_path)
files = np.sort(files)
img_labels = []
samples_path = []
for txt in files:
n = int(txt.split('.')[0][-1]) # obtem a classe da imagem pelo ultimo caracter do nome do arquivo
img_labels.append(int(n))
samples_path.append(os.path.join(images_path,txt))
img_labels = np.array(img_labels)
samples_path = np.array(samples_path)
#col_img = imread_collection(dir_path)
#col_img = col_img.concatenate()
train_path = np.stack((samples_path,img_labels), axis=1) #une os vetores como sendo colunas de uma matriz
#print(train_path)
#print(np.shape(train_path))
test_path = ''
class_index = 1
join_data = False
size_batch = int(len(img_labels) * 0.2)
class2drop = 2
if dataset_name == 'vae_ceratocystis20' and vae == True:
dir_path = 'data/train_images/dataset_eucapytus/dataset-20/*.png'
images_path = 'data/train_images/dataset_eucapytus/dataset-20'
files = os.listdir(images_path)
files = np.sort(files)
img_labels = []
for txt in files:
n = int(txt.split('.')[0][-1]) # obtem a classe da imagem pelo ultimo caracter do nome do arquivo
img_labels.append(int(n + 1))
img_labels = np.array(img_labels)
col_img = imread_collection(dir_path)
col_img = col_img.concatenate()
print('\nRunning VAE to generate latent variables...\n')
vae_model = VAE(col_img, img_labels, epoch=vae_epoch, lat_dim=lat_dim, shape=np.shape(col_img[0]),
len_train=len_train) # len_train --> tamanho do conjunto de treino
data = vae_model.output
print('\nVAE has finished!!\n')
data_dir = os.path.join(script_dir, 'data/' + dataset_name + '_' + str(lat_dim) + 'D')
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
train_path = 'data/' + dataset_name + '_' + str(lat_dim) + 'D' + '/' + dataset_name+ '_' + str(
lat_dim) + 'D.csv'
data.to_csv(train_path, index=False, header=False)
test_path = ''
class_index = lat_dim
join_data = False
size_batch = int(len(img_labels) * 0.2)
class2drop = 3
if dataset_name == 'vae_ceratocystis10' and vae == True:
dir_path = 'data/train_images/dataset_eucapytus/dataset-10/*.png'
images_path = 'data/train_images/dataset_eucapytus/dataset-10'
files = os.listdir(images_path)
files = np.sort(files)
img_labels = []
for txt in files:
n = int(txt.split('.')[0][-1]) # obtem a classe da imagem pelo ultimo caracter do nome do arquivo
img_labels.append(int(n + 1))
img_labels = np.array(img_labels)
col_img = imread_collection(dir_path)
col_img = col_img.concatenate()
print('\nRunning VAE to generate latent variables...\n')
vae_model = VAE(col_img, img_labels, epoch=vae_epoch, lat_dim=lat_dim, shape=np.shape(col_img[0]),
len_train=len_train) # len_train --> tamanho do conjunto de treino
data = vae_model.output
print('\nVAE has finished!!\n')
data_dir = os.path.join(script_dir, 'data/' + dataset_name + '_' + str(lat_dim) + 'D')
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
train_path = 'data/' + dataset_name + '_' + str(lat_dim) + 'D' + '/' + dataset_name+ '_' + str(
lat_dim) + 'D.csv'
data.to_csv(train_path, index=False, header=False)
test_path = ''
class_index = lat_dim
join_data = False
size_batch = int(len(img_labels) * 0.2)
class2drop = 3
if dataset_name == 'vae_ceratocystis5' and vae == True:
dir_path = 'data/train_images/dataset_eucapytus/dataset-5/*.png'
images_path = 'data/train_images/dataset_eucapytus/dataset-5'
files = os.listdir(images_path)
files = np.sort(files)
img_labels = []
for txt in files:
n = int(txt.split('.')[0][-1]) # obtem a classe da imagem pelo ultimo caracter do nome do arquivo
img_labels.append(int(n + 1))
img_labels = np.array(img_labels)
col_img = imread_collection(dir_path)
col_img = col_img.concatenate()
print('\nRunning VAE to generate latent variables...\n')
vae_model = VAE(col_img, img_labels, epoch=vae_epoch, lat_dim=lat_dim, shape=np.shape(col_img[0]),
len_train=len_train) # len_train --> tamanho do conjunto de treino
data = vae_model.output
print('\nVAE has finished!!\n')
data_dir = os.path.join(script_dir, 'data/' + dataset_name + '_' + str(lat_dim) + 'D')
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
train_path = 'data/' + dataset_name + '_' + str(lat_dim) + 'D' + '/' + dataset_name+ '_' + str(
lat_dim) + 'D.csv'
data.to_csv(train_path, index=False, header=False)
test_path = ''
class_index = lat_dim
join_data = False
size_batch = int(len(img_labels) * 0.2)
class2drop = 3
if dataset_name == 'vae_ceratocystis2' and vae == True:
dir_path = 'data/train_images/dataset_eucapytus/dataset-2/*.png'
images_path = 'data/train_images/dataset_eucapytus/dataset-2'
files = os.listdir(images_path)
files = np.sort(files)
img_labels = []
for txt in files:
n = int(txt.split('.')[0][-1]) # obtem a classe da imagem pelo ultimo caracter do nome do arquivo
img_labels.append(int(n + 1))
img_labels = np.array(img_labels)
col_img = imread_collection(dir_path)
col_img = col_img.concatenate()
print('\nRunning VAE to generate latent variables...\n')
vae_model = VAE(col_img, img_labels, epoch=vae_epoch, lat_dim=lat_dim, shape=np.shape(col_img[0]),
len_train=len_train) # len_train --> tamanho do conjunto de treino
data = vae_model.output
print('\nVAE has finished!!\n')
data_dir = os.path.join(script_dir, 'data/' + dataset_name + '_' + str(lat_dim) + 'D')
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
train_path = 'data/' + dataset_name + '_' + str(lat_dim) + 'D' + '/' + dataset_name+ '_' + str(
lat_dim) + 'D.csv'
data.to_csv(train_path, index=False, header=False)
test_path = ''
class_index = lat_dim
join_data = False
size_batch = int(len(img_labels) * 0.2)
class2drop = 3
if dataset_name == 'vae_ceratocystis1' and vae == True:
dir_path = 'data/train_images/dataset_eucapytus/dataset-1/*.png'
images_path = 'data/train_images/dataset_eucapytus/dataset-1'
files = os.listdir(images_path)
files = np.sort(files)
img_labels = []
for txt in files:
n = int(txt.split('.')[0][-1]) # obtem a classe da imagem pelo ultimo caracter do nome do arquivo
img_labels.append(int(n + 1))
img_labels = np.array(img_labels)
col_img = imread_collection(dir_path)
col_img = col_img.concatenate()
print('\nRunning VAE to generate latent variables...\n')
vae_model = VAE(col_img, img_labels, epoch=vae_epoch, lat_dim=lat_dim, shape=np.shape(col_img[0]),
len_train=len_train) # len_train --> tamanho do conjunto de treino
data = vae_model.output
print('\nVAE has finished!!\n')
data_dir = os.path.join(script_dir, 'data/' + dataset_name + '_' + str(lat_dim) + 'D')
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
train_path = 'data/' + dataset_name + '_' + str(lat_dim) + 'D' + '/' + dataset_name + '_' + str(
lat_dim) + 'D.csv'
data.to_csv(train_path, index=False, header=False)
test_path = ''
class_index = lat_dim
join_data = False
size_batch = int(len(img_labels) * 0.2)
class2drop = 3
if dataset_name == 'hc_ceratocystis20' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/hc_features/ceratocystis20.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(6543 * 0.2)
class2drop = 3
if dataset_name == 'hc_ceratocystis10' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/hc_features/ceratocystis10.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(4070 * 0.2)
class2drop = 3
if dataset_name == 'hc_ceratocystis5' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/hc_features/ceratocystis5.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(2667 * 0.2)
class2drop = 3
if dataset_name == 'hc_ceratocystis2' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/hc_features/ceratocystis2.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(1183 * 0.2)
class2drop = 3
if dataset_name == 'hc_ceratocystis1' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/hc_features/ceratocystis1.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(689 * 0.2)
class2drop = 3
if dataset_name == 'dp_ceratocystis20' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/dp_features/ceratocystis20.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(6543 * 0.2)
class2drop = 3
if dataset_name == 'dp_ceratocystis10' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/dp_features/ceratocystis10.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(4070 * 0.2)
class2drop = 3
if dataset_name == 'dp_ceratocystis5' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/dp_features/ceratocystis5.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(2667 * 0.2)
class2drop = 3
if dataset_name == 'dp_ceratocystis1' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/dp_features/ceratocystis1.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(689*0.2)
class2drop = 3
if dataset_name == 'dp_ceratocystis2' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Eucaliyptus_dataset/main/dp_features/ceratocystis2.csv'
test_path = ''
class_index = 8
join_data = False
size_batch = int(1183 * 0.2)
class2drop = 3
if dataset_name == 'mnist' and vae == True:
(train, train_labels), (test, test_labels) = keras.datasets.mnist.load_data()
print(type(train))
features = np.concatenate((train, test), axis=0)
img_labels = np.concatenate((train_labels, test_labels), axis=0)
print('\nRunning VAE to generate latent variables...\n')
vae_model = VAE(features, img_labels, epoch=vae_epoch, lat_dim= lat_dim, shape =(28,28,1),
len_train=len_train) # len_train --> tamanho do conjunto de treino
data = vae_model.output
print('\nVAE has finished!!\n')
data_dir = os.path.join(script_dir, 'data/' + dataset_name+'_VAE_'+str(lat_dim*2)+'D')
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
train_path = 'data/' + dataset_name+'_VAE_'+str(lat_dim*2)+'D' + '/' + 'mnist_VAE_'+str(lat_dim*2)+'D.csv'
data.to_csv(train_path, index=False)
test_path = ''
class_index = (lat_dim)
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 0
if dataset_name == 'mnist2D_c0' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 0
if dataset_name == 'mnist2D_c1' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 1
if dataset_name == 'mnist2D_c2' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 2
if dataset_name == 'mnist2D_c3' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 3
if dataset_name == 'mnist2D_c4' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 4
if dataset_name == 'mnist2D_c5' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 5
if dataset_name == 'mnist2D_c6' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 6
if dataset_name == 'mnist2D_c7' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 7
if dataset_name == 'mnist2D_c8' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 8
if dataset_name == 'mnist2D_c9' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/mnist_2d_tsne.csv'
test_path = ''
class_index = 2
join_data = False
size_batch = int(70000 * 0.2)
class2drop = 9
if dataset_name == 'mnist2D' and vae == False:
train_path = 'https://raw.githubusercontent.com/Mailson-Silva/weka-dataset/main/mnist_train_2d_weka.csv'
test_path = 'https://raw.githubusercontent.com/Mailson-Silva/weka-dataset/main/mnist_test_2d_weka.csv'
'''
train, train_labels, test, test_labels = ft.separate_features_and_labels(train_path,
test_path,
class_index=2,
class2drop= 0)
'''
class_index = 2
join_data = True
size_batch = int(70000 * 0.2)
class2drop = 1
if dataset_name == 'mnist4D' and vae == False:
train_path= 'https://raw.githubusercontent.com/Mailson-Silva/mnist_lalent_features/main/mnist_train'
test_path = 'https://raw.githubusercontent.com/Mailson-Silva/mnist_lalent_features/main/mnist_test'
'''
class_index = 4
df_training = pd.read_csv(train_data_path, header=None,
names=['z_mean1', 'z_mean2', 'z_log_var1', 'z_log_var2', 'labels'])
df_valores = df_training.loc[df_training['labels'] == 0]
df_training.drop(df_valores.index, inplace=True)
feat_index = list(range(df_training.shape[1]))
feat_index.remove(class_index)
train = df_training.iloc[:, feat_index].values
train_labels = df_training.iloc[:, class_index].values
df_test = pd.read_csv(test_data_path, header=None,
names=['z_mean1', 'z_mean2', 'z_log_var1', 'z_log_var2', 'labels'])
feat_index = list(range(df_test.shape[1]))
feat_index.remove(class_index)
test = df_test.iloc[:, feat_index].values
test_labels = df_test.iloc[:, class_index].values
'''
class_index = 4
join_data = True
size_batch = int(70000 * 0.2)
class2drop = 1
if dataset_name == 'iris' and vae == False:
train_path= 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/iris2d-train.csv'
test_path = 'https://raw.githubusercontent.com/Mailson-Silva/Dataset/main/iris2d-test.csv'
class_index = 2
join_data = True
size_batch = int(150 * 0.2)
class2drop = -1
return train_path, test_path, class_index, join_data, size_batch, class2drop
# Algoritmo de self-training
def self_training(iter, model_name, train, train_labels, test, test_labels, metric,
list_new_class_labels, results_dir, n_test_class=10, kmeans_graph=False):
# cria variáveis para execução do self-training e gera a pasta para armazenar os resultados
#----------------------------------------------------------------
x_axis = []
y_acc = []
y_precisao = []
y_recall = []
y_fscore = []
y_prauc = []
pseudopoints = []
erro_das_classes = []
erro_da_classe_por_rodada = []
time_classifier = []
time_metric = []
prop_por_rodada = []
curva_sel_por_rodada = []
ob = 0.5 * np.shape(train)[0]
#curva_sel = []
#prop_por_classe = []
if len(train[0]) == 2:
print(results_dir)
#results_dir = os.path.join(results_dir, '/'+metric+'_objects_selected')
results_dir = results_dir + '/' + metric + '_objects_selected'
#print(results_dir)
if not os.path.isdir(results_dir):
os.makedirs(results_dir)
test_original = test
labels_original = test_labels
#--------------------------------------------------------------
print('\n*******************************************')
print(' Starting Self-training procedure... ')
print(' Classifier: ' + str(model_name) + ' - Metric: ' + str(metric) )
print('*********************************************')
#retira os objetos da(s) classe(s) nova(s) do conj. de teste para acrescentar depois
#-----------------------------------------
all_labels = np.concatenate((train_labels, test_labels), axis=0)
test,test_labels = ft.sort_testset(test,test_labels) # ordena os dados de teste de acordo com a classe
new_classes_objs, test,test_labels = ft.draw_new_classes(list_new_class_labels, test, test_labels) #retira a classe nova do conjunto de teste
if (model_name == 'IC_EDS'):
[silhouette_list, clusterers_list, cluslabels_list, nuclusters_list, SSet, matDist] = ft.clusterEnsemble(test)
# Modelo incremental
elif (model_name == 'iCaRL'):
SSet = []
nb_exemplars = int(np.ceil(60 / len(np.unique(train_labels))))
pseudopoints, n_centros, pred_train, kmeans_train_center, train_center_dists = ft.get_pseudopoints(train)
mean_old_class, old_class = ft.get_mean_class(train, train_labels)
train, train_labels = ft.select_exemplars(train, train_labels, mean_old_class, nb_exemplars, old_class)
else:
SSet = []
#train, train_labels = ft.sel_exemplares(train, train_labels, ob)
if model_name == 'deepncm':
# FIX SEEDS.
import random
torch.manual_seed(1993)
torch.cuda.manual_seed(1993)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(1993)
random.seed(1993)
device = 'cpu'
known_classes = len(np.unique(train_labels))
updated_known_classes = np.unique(train_labels)
batch_size = 15
transform_train, transform_basic, transform_val, transform_test,start, end = ft.make_dataset()
# set up network
network = ResNet18(classes= len(np.unique(train_labels)), pretrained=None, relu=not False,
deep_nno=True).to(device)
# setup trainer
trainer = Trainer(batch_size, network,device) #batch size = 15
train_set = RODFolder(train, train_labels, target_transform=None, transform=transform_train)
trainloader = DataLoader(train_set, batch_size=batch_size, shuffle=True,
num_workers=2, sampler=None)
# Setup Network
#network.to(device)
# Start iteration
print(f'Start trainning ResNet...')
# Setup epoch and optimizer
epochs = 12 # epochs é 12 ou 6
epochs_strat = [80]
learning_rate = 0.05 # opts.lr #pode ser modificado
lr_factor = 0.1
wght_decay = 0.0
optimizer = optim.SGD(filter(lambda p: p.requires_grad, network.parameters()), lr=learning_rate,
momentum=0.9, weight_decay=wght_decay, nesterov=False)
lr_strat = [epstrat * epochs // 100 for epstrat in epochs_strat]
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=lr_strat, gamma=lr_factor)
# lr_strat e lr_factor podem ser ajustados
#print(f'\n learning rate: {learning_rate}\n lr strat: {lr_strat}\n lr_factor: {lr_factor}\n features_dw: {opts.features_dw}\n')
subset_trainloader = []
for epoch in range(epochs):
trainer.train(epoch, trainloader, subset_trainloader, optimizer, updated_known_classes, 0)
scheduler.step()
##############
#teste
network.eval()
test_set = RODFolder(test, test_labels, target_transform=None, transform=transform_test)
testloader = DataLoader(test_set, batch_size=len(test_labels), shuffle=True,
num_workers=2, sampler=None)
for (inputs, targets_prep) in testloader:
test_loaded = inputs
test_labels = targets_prep
test_loaded = test_loaded.to(device)
outputs, _ = network(test_loaded)
outputs, _, distances = network.predict(outputs)
test_labels = test_labels.to(outputs.device)
# changes the probabilities (distances) for known and unknown
new_outputs = trainer.reject(outputs, distances, trainer.tau)
# print(new_outputs)
_, preds = new_outputs.max(1)
# indices = np.where(preds == known_classes)[0]
sel_objs = [] # armazena os indices dos objetos preditos como oriundos de classe desconhecida
score_objs = [] # armazena o 'score' dos objetos de classe desconhecida
# sel_objs.extend(indices)
new_outputs_copy = new_outputs.detach().cpu().numpy()
score_objs.extend(new_outputs_copy[:, len(np.unique(train_labels))]) # score_objs.extend(new_outputs_copy[indices, known_classes])
sel_objs = np.array(sel_objs)
score_objs = np.array(score_objs)
sorted_score = score_objs.argsort() # encontra o indice dos objetos com os 5 maiores scores
e = score_objs[sorted_score]
classifier_results = alghms(model_name, train, train_labels, test, test_labels, None, results_dir,
n_test_class, 0, kmeans_graph, SSet, pseudopoints)
elif model_name == 'NNO':
device = 'cpu'
known_classes = len(np.unique(train_labels))
updated_known_classes = np.unique(train_labels)
NNO = NCM_classifier(len(train[0]),known_classes,True)
tau = Parameter(torch.tensor([0.5], device=device), requires_grad=True)
deep_nno_tau = DeepNNO(tau, device, factor=2.0, bm=False)
print(f'Tau original: {tau}')
torch_train = torch.from_numpy(train)
NNO.update_means(torch_train, train_labels)
prediction, exp, distances = NNO.forward(torch_train)
#print(f'PREDICTION (PROBS): {prediction}')
deep_nno_tau.update_taus(prediction, train_labels,len(np.unique(train_labels))) ###
print(f'Tau novo: {tau}')
outputs, _, distances = NNO.forward(torch.from_numpy(test))
_, preds = outputs.max(1) ####
preds = preds + 1
acuracia = accuracy_score(test_labels, preds)
#print(f'(antes) DESEMPENHO NO TEST SET {acuracia} e PREDS: {preds}')
#print(f'OUTPUTS (PROBS): {outputs}')
new_outputs = NNO.reject(outputs, distances, tau)
_, preds = new_outputs.max(1) ####
preds = preds +1
acuracia = accuracy_score(test_labels, preds)
#print(f'(depois) DESEMPENHO NO TEST SET {acuracia} e PREDS: {preds}')
#print('labels: ', test_labels)
#print(f'preds: {preds}')
#indices = np.where(preds == known_classes)[0]
sel_objs = [] #armazena os indices dos objetos preditos como oriundos de classe desconhecida
score_objs = [] #armazena o 'score' dos objetos de classe desconhecida
#sel_objs.extend(indices)
new_outputs_copy = new_outputs.detach().cpu().numpy()
score_objs.extend(new_outputs_copy[:, known_classes]) #score_objs.extend(new_outputs_copy[indices, known_classes])
sel_objs = np.array(sel_objs)
score_objs = np.array(score_objs)
sorted_score = score_objs.argsort() # encontra o indice dos objetos com os 5 maiores scores
e = score_objs[sorted_score]
'''
indices = np.where(predicted == last_class)[0]
sel_objs.extend(indices)
new_outputs_copy = new_outputs.detach().cpu().numpy()
score_objs.extend(new_outputs_copy[indices, last_class])
'''
classifier_results = alghms(model_name, train, train_labels, test, test_labels, None, results_dir,
n_test_class, 0, kmeans_graph, SSet, pseudopoints)
else:
classifier_results = alghms(model_name, train, train_labels, test, test_labels, metric, results_dir,
n_test_class, 0, kmeans_graph, SSet, pseudopoints)
preds = classifier_results.pred
probs = classifier_results.probs
e = classifier_results.e
acuracia = accuracy_score(test_labels, preds)
precisao = precision_recall_fscore_support(test_labels, preds, average='weighted')[0]
recall = precision_recall_fscore_support(test_labels, preds, average='weighted')[1]
f_score = precision_recall_fscore_support(test_labels, preds, average='weighted')[2]
# TESTE DA METRICA PRC AUC:
if len(np.unique(test_labels)) == 2:
one_hot_test_labels = []
for i in range(len(test_labels)):
if test_labels[i] == 1 or test_labels[i]== [1]:
one_hot_test_labels.append([1,0])
else:
one_hot_test_labels.append([0,1])
one_hot_test_labels = np.array(one_hot_test_labels)
else:
one_hot_test_labels = label_binarize(test_labels, classes=np.unique(test_labels))
pr_auc = average_precision_score(one_hot_test_labels, probs,average="micro")
print(pr_auc)
#########################################
for i in np.unique(labels_original):
erro = ft.class_error(preds, test_labels, i)
erro_da_classe_por_rodada.append(erro)
erro_das_classes.append(erro_da_classe_por_rodada.copy())
erro_da_classe_por_rodada.clear()
x_axis.append(0)
y_acc.append(acuracia)
y_precisao.append(precisao)
y_recall.append(recall)
y_fscore.append(f_score)
y_prauc.append(pr_auc)
print("\nIteration " + str(0) + " (before train set increment) - Sizes: Training Set " + str(len(train)) + " - Test Set " + str(len(test)) +
" - Accuracy: " + str(round(acuracia,4)) )
print('Elapsed time (seg) of classifier ' + str(model_name)+' :' + str(round(classifier_results.classifier_time, 4)))
print('Elapsed time (seg) of metric ' + str(metric)+' :' + str(round(classifier_results.metric_time, 4)))
time_metric.append(-1)#round(classifier_results.metric_time, 4))
time_classifier.append(round(classifier_results.classifier_time, 4))
for k in range(1, iter + 1): # 11):
count_nc = 0
if len(test) > 0:
print('\nIteraction: ' + str(k) +' | Running train set increment...')
if (model_name != 'IC_EDS' and (metric != 'random' and metric != 'aleatória')):
#curva_sel.append(e.copy())
#graph.plot_metric_sel(e, metric, results_path, k)
curva_sel_por_rodada.append(e.copy())
# https://scikit-learn.org/stable/modules/svm.html
if(metric == 'EDS'):
w = ft.eds(e[0], e[1], 5, SSet)
elif(metric == 'random' or metric == 'aleatória'):
w = np.random.choice(range(0, test_labels.shape[0]), 5, replace=False)
elif(model_name == 'NNO' or model_name == 'deepncm'):
#w = sel_objs[sorted_score[-5:][::-1]]
w = sorted_score[-5:][::-1]
print(w)
else:
df_e = pd.DataFrame(e)
df_e.sort_values(by=[0], inplace=True, ascending=False)
# print(df_e)
# funcao q a partir de e retorna um w que sao os indices dos c mais confiaveis
posicoes = df_e.index.values
posicoes = posicoes.tolist()
p = 5 # 96
w = posicoes[0:p] # posicoes[0:p] # índices (posição) dos objetos que serão retirados do conjunto de teste e colocados no conjunto de treino
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
[train, train_labels, test, test_labels, objects_labels] = ft.increment_training_set(w, train, train_labels, test,
test_labels, k, results_dir)
test, test_labels = ft.sort_testset(test, test_labels)
if len(objects_labels) > 0 or objects_labels != []:
#all_labels = np.concatenate((train_labels, test_labels), axis=0)
pro = ft.class_proportion_objects(objects_labels, all_labels)
prop_por_rodada.append(pro.copy())
# ft.visualize_data(train, train_labels,[])]
if len(test) > 0:
# https://scikit-learn.org/stable/modules/svm.html
if(model_name=='IC_EDS'):
SSet = ft.reduce_matrix(w, SSet)
#else:
# Modelo incremental
#train, train_labels = ft.sel_exemplares(train, train_labels, ob)
# código que faz a inserção da nova classe durante a execução do modelo
for nc in list_new_class_labels:
if nc[0] == k:
test = np.concatenate((test, np.squeeze(new_classes_objs[count_nc][0])), axis=0)
test_labels = np.concatenate(
(test_labels, np.expand_dims(new_classes_objs[count_nc][1], axis=1)))
if (model_name == 'IC_EDS'):
[silhouette_list, clusterers_list, cluslabels_list, nuclusters_list, SSet,
matDist] = ft.clusterEnsemble(test)
count_nc += 1
if model_name == 'iCaRL':
nb_exemplars = int(np.ceil(60 / len(np.unique(train_labels))))
pseudopoints = ft.upg_pseudopoints(old_class, pseudopoints, train, train_labels)
old_class, mean_old_class = ft.upg_means(old_class, mean_old_class, train, train_labels, nb_exemplars)
train, train_labels = ft.select_exemplars(train, train_labels, mean_old_class, nb_exemplars, old_class)
classifier_results = alghms(model_name, train, train_labels, test, test_labels, metric, results_dir,
n_test_class, k, kmeans_graph, SSet, pseudopoints)
preds = classifier_results.pred
probs = classifier_results.probs
e = classifier_results.e
elif model_name == 'deepncm':
new_class = [x for x in np.unique(train_labels) if x not in updated_known_classes]
train_labels = train_labels.flatten()
train = train.flatten()
train_set = RODFolder(train, train_labels, target_transform=None, transform=transform_test)
trainloader = DataLoader(train_set, batch_size=batch_size, shuffle=True,
num_workers=2, sampler=None)
trainer.network2 = copy.deepcopy(network)
# Setup Network
if new_class != []:
trainer.next_iteration(len(new_class)) # adiciona novas classes ao deepNCM
network.to(device)
updated_known_classes = np.append(updated_known_classes, new_class)
# network.to(device)
# Start iteration
print(f'Start trainning ResNet...')
# Setup epoch and optimizer
epochs = 6 # epochs é 12 ou 6
epochs_strat = [80]
learning_rate = 0.05 # opts.lr #pode ser modificado
lr_factor = 0.1
wght_decay = 0.0
optimizer = optim.SGD(filter(lambda p: p.requires_grad, network.parameters()), lr=learning_rate,
momentum=0.9, weight_decay=wght_decay, nesterov=False)
lr_strat = [epstrat * epochs // 100 for epstrat in epochs_strat]
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=lr_strat, gamma=lr_factor)
# lr_strat e lr_factor podem ser ajustados
# print(f'\n learning rate: {learning_rate}\n lr strat: {lr_strat}\n lr_factor: {lr_factor}\n features_dw: {opts.features_dw}\n')
subset_trainloader = []
for epoch in range(epochs):
trainer.train(epoch, trainloader, subset_trainloader, optimizer, updated_known_classes, k)
scheduler.step()
##############
# teste
network.eval()
test_labels = test_labels.flatten()
test = test.flatten()
test_set = RODFolder(test, test_labels, target_transform=None, transform=transform_test)
testloader = DataLoader(test_set, batch_size=len(test_labels), shuffle=True,
num_workers=2, sampler=None)
for (inputs, targets_prep) in testloader:
test_loaded = inputs
test_labels = targets_prep
test_loaded = test_loaded.to(device)
outputs, _ = network(test_loaded)
outputs, _, distances = network.predict(outputs)
test_labels = test_labels.to(outputs.device)
# changes the probabilities (distances) for known and unknown
new_outputs = trainer.reject(outputs, distances, trainer.tau)
# print(new_outputs)
_, preds = new_outputs.max(1)
# indices = np.where(preds == known_classes)[0]
sel_objs = [] # armazena os indices dos objetos preditos como oriundos de classe desconhecida
score_objs = [] # armazena o 'score' dos objetos de classe desconhecida
# sel_objs.extend(indices)
new_outputs_copy = new_outputs.detach().cpu().numpy()
score_objs.extend(new_outputs_copy[:, len(np.unique(
train_labels))]) # score_objs.extend(new_outputs_copy[indices, known_classes])
sel_objs = np.array(sel_objs)
score_objs = np.array(score_objs)
sorted_score = score_objs.argsort() # encontra o indice dos objetos com os 5 maiores scores
e = score_objs[sorted_score]
elif model_name == 'NNO':