def plot_embedding(self,
resultpath=None,
algos=None,
show_label=False,
disp_num_r_n_e=20):
"""Function to plot the embedding.
Args:
resultpath (str): Path where the result will be saved.
show_label (bool): If True, will display the labels.
algos (str): Name of the algorithms that generated the embedding.
disp_num_r_n_e (int): Total number of entities to display for head, tail and relation.
"""
assert self.model is not None, 'Please provide a model!'
if self.ent_only_plot:
x = torch.cat(self.h_emb + self.t_emb, dim=0)
ent_names = np.concatenate((self.h_name, self.t_name), axis=0)
self._logger.info("\t Reducing dimension using TSNE to 2!")
x = TSNE(n_components=2).fit_transform(x.detach().cpu())
x = np.asarray(x)
ent_names = np.asarray(ent_names)
self.draw_embedding(x, ent_names, resultpath,
algos + '_entity_plot', show_label)
if self.rel_only_plot:
x = torch.cat(self.r_emb, dim=0)
self._logger.info("\t Reducing dimension using TSNE to 2!")
x = TSNE(n_components=2).fit_transform(x.detach().cpu())
self.draw_embedding(x, self.r_name, resultpath,
algos + '_rel_plot', show_label)
if self.ent_and_rel_plot:
length = len(self.h_proj_emb)
x = torch.cat(self.h_proj_emb + self.r_proj_emb + self.t_proj_emb,
dim=0)
self._logger.info("\t Reducing dimension using TSNE to 2!")
x = TSNE(n_components=2).fit_transform(x.detach().cpu())
h_embs = x[:length, :]
r_embs = x[length:2 * length, :]
t_embs = x[2 * length:3 * length, :]
self.draw_embedding_rel_space(
h_embs[:disp_num_r_n_e], r_embs[:disp_num_r_n_e],
t_embs[:disp_num_r_n_e], self.h_name[:disp_num_r_n_e],
self.r_name[:disp_num_r_n_e], self.t_name[:disp_num_r_n_e],
resultpath, algos + '_ent_n_rel_plot', show_label)
def plot_points(embedding, colors=None):
# plots the embedding using the classes defined in data.y
reset_random() # only needed for TSNE
if colors == None:
colors = [
'#ffc0cb', '#bada55', '#008080', '#420420', '#7fe5f0', '#065535',
'#ffd700'
]
print(np.shape(embedding))
if np.shape(embedding)[1] > 2:
embedding = TSNE(n_components=2).fit_transform(
embedding.detach().cpu().numpy())
y = data.y.cpu().numpy()
plt.figure(figsize=(8, 8))
for i in range(dataset.num_classes):
plt.scatter(embedding[y == i, 0],
embedding[y == i, 1],
s=20,
color=colors[i])
plt.axis('off')
plt.show()
for i, ((source_data, source_label),
(target_data,
_)) in enumerate(zip(source_dataloader, target_dataloader)):
source_data = source_data.cuda()
source_label = source_label.cuda()
target_data = target_data.cuda()
# 我們把source data和target data混在一起，否則batch_norm可能會算錯 (兩邊的data的mean/var不太一樣)
mixed_data = torch.cat([source_data, target_data], dim=0)
domain_label = torch.zeros(
[source_data.shape[0] + target_data.shape[0], 1]).cuda()
# 設定source data的label為1
domain_label[:source_data.shape[0]] = 1
domain_label = TSNE(n_components=2).fit_transform(
domain_label.detach().cpu().numpy())
# Step 1 : 訓練Domain Classifier
feature = feature_extractor(mixed_data)
# domain_logits = domain_classifier(feature.detach())
feature = TSNE(n_components=2).fit_transform(
feature.detach().cpu().numpy())
feature_extract.append(feature)
answer.append(domain_label)
# = TSNE(n_components=2).fit_transform(kpca)
# class_logits = label_predictor(feature[:source_data.shape[0]])
# domain_logits = domain_classifier(feature)
# loss = domain_criterion(domain_logits, domain_label)
# running_D_loss+= loss.item()
# loss.backward()
# optimizer_D.step()
import numpy as np
from DataManager import DataManager
from util import TRAIN, SMALL
from sklearn.neighbors import NearestNeighbors
model = torch.load('.//data//budala_16.pickle',
map_location=torch.device('cpu'))
#data_manager_train = DataManager()
#tag_to_idx, idx_to_tag = data_manager_train.get_tag_dicts()
embedding = model.module.tag_embeddings.weight
embedding = TSNE(n_components=2).fit_transform(
embedding.detach().long().numpy())
embedding_with = np.array([[x, y, i] for i, (x, y) in enumerate(embedding)],
dtype=np.int32)
plt.scatter(embedding[:, 0], embedding[:, 1])
# nearest neighbors
# nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(embedding.detach().long().numpy())
# distances, indices = nbrs.kneighbors(embedding.detach().long().numpy())
# print(indices)
# embeddings in 2D
x = embedding[:, 0]
y = embedding[:, 1]
names = np.array(list("ABCDEFGHIJKLMNO"))
c = np.random.randint(1, 5, size=15)
