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eval_text.py
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eval_text.py
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# --- Base packages ---
import os
import numpy as np
import matplotlib.pyplot as plt
import sklearn.metrics as metrics
# --- PyTorch packages ---
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import torchvision.models as models
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# --- Project Packages ---
from utils import save, load, train, test
from datasets import MIMIC, NLMCXR, TextDataset
from models import Classifier, TNN
from baselines.transformer.models import LSTM_Attn
# --- Hyperparameters ---
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
os.environ["OMP_NUM_THREADS"] = "1"
torch.set_num_threads(1)
torch.manual_seed(seed=0)
DATASET_NAME = 'MIMIC' # MIMIC / NLMCXR
MODEL_NAME = 'LSTM' # Transformer / LSTM
BATCH_SIZE = 32
TEXT_FILE = 'outputs/{}_ClsGen_DenseNet121_MaxView2_NumLabel114_NoHistory_Hyp.txt'.format(DATASET_NAME)
LABEL_FILE = 'outputs/{}_ClsGen_DenseNet121_MaxView2_NumLabel114_NoHistory_Lbl.txt'.format(DATASET_NAME)
if __name__ == "__main__":
# --- Choose Inputs/Outputs
if MODEL_NAME == 'Transformer':
SOURCES = ['caption']
TARGETS = ['label']
KW_SRC = ['txt'] # kwargs of Classifier
KW_TGT = None
KW_OUT = None
elif MODEL_NAME == 'LSTM':
SOURCES = ['caption', 'caption_length']
TARGETS = ['label']
KW_SRC = ['caption', 'caption_length'] # kwargs of LSTM_Attn
KW_TGT = None
KW_OUT = None
else:
raise ValueError('Invalid MODEL_NAME')
# --- Choose a Dataset ---
if DATASET_NAME == 'MIMIC':
INPUT_SIZE = (256,256)
MAX_VIEWS = 2
NUM_LABELS = 114 # (14 diseases + 100 top noun-phrases)
NUM_CLASSES = 2
dataset = TextDataset(text_file=TEXT_FILE, label_file=LABEL_FILE, sources=SOURCES, targets=TARGETS,
vocab_file='/home/hoang/Datasets/MIMIC/mimic_unigram_1000.model', max_len=1000)
VOCAB_SIZE = len(dataset.vocab)
POSIT_SIZE = dataset.max_len
COMMENT = 'MaxView{}_NumLabel{}'.format(MAX_VIEWS, NUM_LABELS)
elif DATASET_NAME == 'NLMCXR':
INPUT_SIZE = (256,256)
MAX_VIEWS = 2
NUM_LABELS = 114 # (14 diseases + 100 top noun-phrases)
NUM_CLASSES = 2
dataset = TextDataset(text_file=TEXT_FILE, label_file=LABEL_FILE, sources=SOURCES, targets=TARGETS,
vocab_file='/home/hoang/Datasets/NLMCXR/nlmcxr_unigram_1000.model', max_len=1000)
VOCAB_SIZE = len(dataset.vocab)
POSIT_SIZE = dataset.max_len
COMMENT = 'MaxView{}_NumLabel{}'.format(MAX_VIEWS, NUM_LABELS)
else:
raise ValueError('Invalid DATASET_NAME')
# --- Choose a Model ---
if MODEL_NAME == 'Transformer':
NUM_EMBEDS = 256
NUM_HEADS = 8
FWD_DIM = 256
NUM_LAYERS = 1
DROPOUT = 0.1
tnn = TNN(embed_dim=NUM_EMBEDS, num_heads=NUM_HEADS, fwd_dim=FWD_DIM, dropout=DROPOUT, num_layers=NUM_LAYERS, num_tokens=VOCAB_SIZE, num_posits=POSIT_SIZE)
model = Classifier(num_topics=NUM_LABELS, num_states=NUM_CLASSES, cnn=None, tnn=tnn, embed_dim=NUM_EMBEDS, num_heads=NUM_HEADS, dropout=DROPOUT)
elif MODEL_NAME == 'LSTM':
# Justin et al. hyper-parameters
NUM_EMBEDS = 256
HIDDEN_SIZE = 128
DROPOUT = 0.1
model = LSTM_Attn(num_tokens=VOCAB_SIZE, embed_dim=NUM_EMBEDS, hidden_size=HIDDEN_SIZE, num_topics=NUM_LABELS, num_states=NUM_CLASSES, dropout=DROPOUT)
else:
raise ValueError('Invalid MODEL_NAME')
# --- Main program ---
data_loader = data.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=8)
model = nn.DataParallel(model).cuda()
checkpoint_path_from = 'checkpoints/{}_{}_{}.pt'.format(DATASET_NAME,MODEL_NAME,COMMENT)
last_epoch, (best_metric, test_metric) = load(checkpoint_path_from, model)
print('Reload From: {} | Last Epoch: {} | Validation Metric: {} | Test Metric: {}'.format(checkpoint_path_from, last_epoch, best_metric, test_metric))
test_loss, test_outputs, test_targets = test(data_loader, model, device='cuda', kw_src=KW_SRC, kw_tgt=KW_TGT, kw_out=KW_OUT)
# --- Evaluation ---
test_auc = []
test_f1 = []
test_prc = []
test_rec = []
test_acc = []
threshold = 0.5
NUM_LABELS = 14
for i in range(NUM_LABELS):
try:
test_auc.append(metrics.roc_auc_score(test_targets.cpu()[...,i], test_outputs.cpu()[...,i,1]))
test_f1.append(metrics.f1_score(test_targets.cpu()[...,i], test_outputs.cpu()[...,i,1] > threshold))
test_prc.append(metrics.precision_score(test_targets.cpu()[...,i], test_outputs.cpu()[...,i,1] > threshold))
test_rec.append(metrics.recall_score(test_targets.cpu()[...,i], test_outputs.cpu()[...,i,1] > threshold))
test_acc.append(metrics.accuracy_score(test_targets.cpu()[...,i], test_outputs.cpu()[...,i,1] > threshold))
except:
print('An error occurs for label', i)
test_auc = np.mean([x for x in test_auc if str(x) != 'nan'])
test_f1 = np.mean([x for x in test_f1 if str(x) != 'nan'])
test_prc = np.mean([x for x in test_prc if str(x) != 'nan'])
test_rec = np.mean([x for x in test_rec if str(x) != 'nan'])
test_acc = np.mean([x for x in test_acc if str(x) != 'nan'])
print('Accuracy : {}'.format(test_acc))
print('Macro AUC : {}'.format(test_auc))
print('Macro F1 : {}'.format(test_f1))
print('Macro Precision: {}'.format(test_prc))
print('Macro Recall : {}'.format(test_rec))
print('Micro AUC : {}'.format(metrics.roc_auc_score(test_targets.cpu()[...,:NUM_LABELS] == 1, test_outputs.cpu()[...,:NUM_LABELS,1], average='micro')))
print('Micro F1 : {}'.format(metrics.f1_score(test_targets.cpu()[...,:NUM_LABELS] == 1, test_outputs.cpu()[...,:NUM_LABELS,1] > threshold, average='micro')))
print('Micro Precision: {}'.format(metrics.precision_score(test_targets.cpu()[...,:NUM_LABELS] == 1, test_outputs.cpu()[...,:NUM_LABELS,1] > threshold, average='micro')))
print('Micro Recall : {}'.format(metrics.recall_score(test_targets.cpu()[...,:NUM_LABELS] == 1, test_outputs.cpu()[...,:NUM_LABELS,1] > threshold, average='micro')))