/
metric_eval.py
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/
metric_eval.py
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import os
import sys
from operator import itemgetter
import sklearn
import sklearn.metrics
import torch
import torch.autograd as autograd
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from eval import show_results
def metric_dist(represent, target):
target_list = target.tolist()
dim = represent.shape[1]
indices = [i for i, x in enumerate(target_list) if x == 1]
other_indices = list(set(range(0, len(target_list))) - set(indices))
loss_intra = 0
num_intra = 0
for i in range(len(indices)):
for j in range(i + 1, len(indices)):
r_i = represent[indices[i]]
r_j = represent[indices[j]]
dist_ij = (r_i - r_j).norm(2)
loss_intra += 1 / dim * (dist_ij * dist_ij)
num_intra += 1
if num_intra > 0:
loss_intra = loss_intra / num_intra
loss_inter = 0
num_inter = 0
for i in indices:
for k in other_indices:
r_i = represent[i]
r_k = represent[k]
dist_ik = (r_i - r_k).norm(2)
loss_inter += 1 / dim * (dist_ik * dist_ik)
num_inter += 1
if num_inter > 0:
loss_inter = loss_inter / num_inter
return loss_intra, loss_inter
def open_dist(val_repr_list, val_target_list, test_repr_list, lambda_inter = 1):
target_list = val_target_list.tolist()
dim = val_repr_list.shape[1]
indices = [i for i, x in enumerate(target_list) if x == 1]
other_indices = list(set(range(0, len(target_list))) - set(indices))
num_intra = len(indices)
num_inter = len(other_indices)
dist_list = []
k = 3
for idx in range(len(test_repr_list)):
repr = test_repr_list[idx]
loss_intra = 0
loss_inter = 0
cur_dist_list = []
for i in indices:
dist_intra = (val_repr_list[i] - repr).norm(2)
cur_dist_list.append(1 / dim * dist_intra * dist_intra)
#loss_intra += 1 / dim * dist_intra * dist_intra
nearest_k = sorted(cur_dist_list)[0:k]
dist = sum(nearest_k) / k
# for i in other_indices:
# dist_inter = (val_repr_list[i] - test_repr).norm(2)
# loss_inter += 1 / dim * dist_inter * dist_inter
#dist = loss_intra / num_intra
dist_list.append(dist)
return dist_list
def open_dist2(val_repr_list, val_target_list, test_repr_list, lambda_inter = 1):
target_list = val_target_list.tolist()
dim = val_repr_list.shape[1]
indices = [i for i, x in enumerate(target_list) if x == 1]
other_indices = list(set(range(0, len(target_list))) - set(indices))
num_intra = len(indices)
num_inter = len(other_indices)
dist_list = []
intra_list = []
inter_list = []
k = 10
for idx in range(len(test_repr_list)):
repr = test_repr_list[idx]
loss_intra = 0
loss_inter = 0
cur_intra_dist = []
for i in indices:
dist_intra = (val_repr_list[i] - repr).norm(2)
cur_intra_dist.append(1 / dim * dist_intra * dist_intra)
intra_nearest_k = sorted(cur_intra_dist)[0:k]
intra_dist = sum(intra_nearest_k) / k
cur_inter_dist = []
for i in other_indices:
dist_inter = (val_repr_list[i] - repr).norm(2)
cur_inter_dist.append(1 / dim * dist_inter * dist_inter)
inter_nearest_k = sorted(cur_inter_dist)[0:k]
inter_dist = sum(inter_nearest_k) / k
#dist = loss_intra / num_intra
intra_list.append(intra_dist)
inter_list.append(inter_dist)
dist_list.append(abs(inter_dist - intra_dist))
return dist_list, intra_list, inter_list
def open_eval2(dataset, x_val, y_val, x_test, y_test, model, args):
print("using open evaluation ...")
model.eval()
# collect the representation and target from test data set
test_repr_list = torch.FloatTensor()
test_target_list = torch.LongTensor()
test_iter = dataset.gen_minibatch(x_test, y_test, args.batch_size, args, shuffle=True)
y_pred = []
for batch in test_iter:
feature, target = batch[0], batch[1]
if args.cuda:
feature, target = feature.cuda(), target.cuda()
logit, represent = model(feature)
test_repr_list = torch.cat([test_repr_list, represent.data.cpu()], 0)
test_target_list = torch.cat([test_target_list, target.data.cpu()], 0)
y_pred_cur = (torch.max(logit, 1)[1].view(target.size()).data).tolist()
y_pred += y_pred_cur
y_truth = test_target_list.tolist()
# apply idk ratio to filter out the uncertain instances.
if args.use_idk:
val_repr_list = torch.FloatTensor()
val_target_list = torch.LongTensor()
val_iter = dataset.gen_minibatch(x_val, y_val, args.batch_size, args, shuffle=True)
for batch in val_iter:
feature, target = batch[0], batch[1]
if args.cuda:
feature, target = feature.cuda(), target.cuda()
logit, represent = model(feature)
val_repr_list = torch.cat([val_repr_list, represent.data.cpu()], 0)
val_target_list = torch.cat([val_target_list, target.data.cpu()], 0)
dist_list, intra_list, inter_list = open_dist2(val_repr_list, val_target_list, test_repr_list)
indices, L_sorted = zip(*sorted(enumerate(dist_list), key=itemgetter(1), reverse=True))
for i in range(len(dist_list)):
print(dist_list[i], intra_list[i], inter_list[i], y_pred[i], y_truth[i], sep='\t')
idk_list = np.arange(0, 0.45, 0.05)
for idk_ratio in idk_list:
print("=== idk_ratio: ", idk_ratio, " ===")
test_num = int(len(L_sorted) * (1 - idk_ratio))
indices_cur = list(indices[:test_num])
y_truth_cur = [y_truth[i] for i in indices_cur]
y_pred_cur = [y_pred[i] for i in indices_cur]
f1_score = show_results(y_truth_cur, y_pred_cur, test_repr_list, test_target_list)
else:
f1_score = show_results(y_truth, y_pred, test_repr_list, test_target_list)
return f1_score
def open_eval(dataset, x_val, y_val, x_test, y_test, model, args):
print("using open evaluation ...")
model.eval()
# collect the representation and target from test data set
test_repr_list = torch.FloatTensor()
test_target_list = torch.LongTensor()
test_iter = dataset.gen_minibatch(x_test, y_test, args.batch_size, args, shuffle=True)
y_pred = []
for batch in test_iter:
feature, target = batch[0], batch[1]
if args.cuda:
feature, target = feature.cuda(), target.cuda()
logit, represent = model(feature)
test_repr_list = torch.cat([test_repr_list, represent.data.cpu()], 0)
test_target_list = torch.cat([test_target_list, target.data.cpu()], 0)
y_pred_cur = (torch.max(logit, 1)[1].view(target.size()).data).tolist()
y_pred += y_pred_cur
y_truth = test_target_list.tolist()
# apply idk ratio to filter out the uncertain instances.
if args.use_idk:
# logit_diff_abs = [abs(x) for x in logit_diff]
# indices, L_sorted = zip(*sorted(enumerate(logit_diff_abs), key=itemgetter(1), reverse=True))
# collect the representation and target from eval data set
val_repr_list = torch.FloatTensor()
val_target_list = torch.LongTensor()
val_iter = dataset.gen_minibatch(x_val, y_val, args.batch_size, args, shuffle=True)
for batch in val_iter:
feature, target = batch[0], batch[1]
if args.cuda:
feature, target = feature.cuda(), target.cuda()
logit, represent = model(feature)
val_repr_list = torch.cat([val_repr_list, represent.data.cpu()], 0)
val_target_list = torch.cat([val_target_list, target.data.cpu()], 0)
dist_list = open_dist(val_repr_list, val_target_list, test_repr_list)
pos_dist = []
neg_dist = []
pos_target = []
neg_target = []
pos_pred = []
neg_pred = []
for i in range(len(dist_list)):
pred = y_pred[i]
if pred == 1:
pos_dist.append(dist_list[i])
pos_target.append(y_truth[i])
pos_pred.append(y_pred[i])
else:
neg_dist.append(dist_list[i])
neg_target.append(y_truth[i])
neg_pred.append(y_pred[i])
pos_indices, pos_dist_sorted = zip(*sorted(enumerate(pos_dist), key=itemgetter(1), reverse=False))
neg_indices, neg_dist_sorted = zip(*sorted(enumerate(neg_dist), key=itemgetter(1), reverse=True))
idk_list = np.arange(0, 0.45, 0.05)
for idk_ratio in idk_list:
print("using idk_ratio", idk_ratio)
pos_num_cur = int(len(pos_dist_sorted) * (1 - idk_ratio))
pos_indices_cur = list(pos_indices[:pos_num_cur])
pos_target_cur = [pos_target[i] for i in pos_indices_cur]
pos_pred_cur = [pos_pred[i] for i in pos_indices_cur]
neg_num_cur = int(len(neg_dist_sorted) * (1 - idk_ratio))
#neg_num_cur = int(len(y_truth) * (1 - idk_ratio))
neg_indices_cur = list(neg_indices[:neg_num_cur])
neg_target_cur = [neg_target[i] for i in neg_indices_cur]
neg_pred_cur = [neg_pred[i] for i in neg_indices_cur]
y_truth_cur = pos_target_cur + neg_target_cur
y_pred_cur = pos_pred_cur + neg_pred_cur
# y_truth_cur = pos_target + neg_target_cur
# y_pred_cur = pos_pred + neg_pred_cur
f1_score = show_results(dataset, y_truth_cur, y_pred_cur, test_repr_list, test_target_list)
else:
f1_score = show_results(dataset, y_truth, y_pred, test_repr_list, test_target_list)
return f1_score