def build_model(params, with_dis):
"""
Build all components of the model.
"""
# source embeddings
src_dico, _src_emb = load_embeddings(params, source=True)
params.src_dico = src_dico
src_emb = nn.Embedding(len(src_dico), params.emb_dim, sparse=True)
src_emb.weight.data.copy_(_src_emb)
# target embeddings
if params.tgt_lang:
tgt_dico, _tgt_emb = load_embeddings(params, source=False)
params.tgt_dico = tgt_dico
tgt_emb = nn.Embedding(len(tgt_dico), params.emb_dim, sparse=True)
tgt_emb.weight.data.copy_(_tgt_emb)
else:
tgt_emb = None
# mapping
mapping = nn.Linear(params.emb_dim, params.emb_dim, bias=False)
if getattr(params, 'map_id_init', True):
mapping.weight.data.copy_(torch.diag(torch.ones(params.emb_dim)))
# normalize embeddings
params.src_mean = normalize_embeddings(src_emb.weight.data,
params.normalize_embeddings)
if params.tgt_lang:
params.tgt_mean = normalize_embeddings(tgt_emb.weight.data,
params.normalize_embeddings)
return src_emb, tgt_emb, mapping
def export(self):
"""
Export embeddings.
"""
params = self.params
# load all embeddings
params.src_dico, src_emb = load_embeddings(params,
source=True,
full_vocab=True)
params.tgt_dico, tgt_emb = load_embeddings(params,
source=False,
full_vocab=True)
# apply same normalization as during training
normalize_embeddings(src_emb,
params.normalize_embeddings,
mean=params.src_mean)
normalize_embeddings(tgt_emb,
params.normalize_embeddings,
mean=params.tgt_mean)
# map source embeddings to the target space
bs = 4096
for i, k in enumerate(range(0, len(src_emb), bs)):
x = Variable(src_emb[k:k + bs], volatile=True)
src_emb[k:k + bs] = self.mapping(x).data
# write embeddings to the disk
export_embeddings(src_emb, tgt_emb, params)
def load_embedding_dict(vocab_path="",
vector_path="",
embeddings_path="",
glove=False,
postspec=False): #i can ignore
"""
>>> _load_embedding_dict()
:param vocab_path:
:param vector_path:
:return: embd_dict
"""
if glove and postspec:
raise ValueError("Glove and postspec cannot both be true")
elif glove:
if os.name == "nt":
embd_dict = utils.load_embeddings(
"C:/Users/anlausch/workspace/embedding_files/glove.6B/glove.6B.300d.txt",
word2vec=False)
else:
embd_dict = utils.load_embeddings(
"/work/anlausch/glove.6B.300d.txt", word2vec=False)
return embd_dict
elif postspec:
embd_dict_temp = utils.load_embeddings(
"/work/anlausch/ft_postspec.txt", word2vec=False)
embd_dict = {}
for key, value in embd_dict_temp.items():
embd_dict[key.split("en_")[1]] = value
assert ("test" in embd_dict)
assert ("house" in embd_dict)
return embd_dict
elif embeddings_path.endswith("p"):
with open(embeddings_path, 'rb') as handle:
embd_dict = pickle.load(handle)
return embd_dict
# elif embeddings_path == "pickleTrue": # todo: add load from pickle file
# with open('./data/embbedding_dict.p', 'rb') as handle:
# embd_dict = pickle.load(handle)
return embd_dict
elif embeddings_path != "":
embd_dict = utils.load_embeddings(embeddings_path, word2vec=False)
return embd_dict
else:
embd_dict = {}
vocab = load_vocab_goran(vocab_path)
vectors = load_vectors_goran(vector_path)
for term, index in vocab.items():
embd_dict[term] = vectors[index]
assert len(embd_dict) == len(vocab)
return embd_dict
def _init(self):
logger.info("Initializing ...")
self.entity2id, self.id2entity, self.entid2tags = utils.generate_entity_property_idx(
self.entityPath)
self.property2id, self.id2property, self.proid2tags = utils.generate_entity_property_idx(
self.propertyPath)
self.entid2tycid = utils.generate_entity_tyc_idx(
self.tycWordsPath, self.entity2id)
self.train2id = utils.generate_data_idx(self.trainPath, self.entity2id,
self.property2id)
self.train2id_set = set([' '.join(map(str, t))
for t in self.train2id]) # use for sampling
self.conid2attrid = utils.generate_conceptid_to_attributesid(
self.conceptAttrPath, self.entity2id, self.property2id,
self.max_attr_size)
self.conAttr2id, self.conAttr2id_set = utils.generate_concept_attributes_idx(
self.conceptAttrPath, self.entity2id, self.property2id)
self.dev2id = utils.generate_data_idx(self.devPath, self.entity2id,
self.property2id)
self.test2id = utils.generate_data_idx(self.testPath, self.entity2id,
self.property2id)
self.test_entity_candidate_ids = utils.read_sample_candidates(
self.test_entity_candi_path, self.entity2id)
self.test_attr_candidate_ids = utils.read_sample_candidates(
self.test_attr_candi_path, self.property2id)
self.sample_ent_cand_ids = utils.read_sample_candidates(
self.sample_ent_candi_path, self.entity2id)
self.sample_attr_cand_ids = utils.read_sample_candidates(
self.sample_attr_candi_path, self.property2id)
self.trainTotal = len(self.train2id)
self.conceptAttrTotal = len(self.conid2attrid)
self.devTotal = len(self.dev2id)
self.testTotal = len(self.test2id)
self.entityTotal = len(self.entity2id)
self.propertyTotal = len(self.property2id)
# tencent init
if self.embeddingPath is not None:
self.ent_embeddings = utils.load_embeddings(
self.entity2id, self.embeddingPath, self.entityTotal,
self.ent_size)
self.rel_embeddings = utils.load_embeddings(
self.property2id, self.embeddingPath, self.propertyTotal,
self.rel_size)
self.dev2id_batches = utils.get_batches(self.dev2id, self.batch_size)
self.test2id_batches = utils.get_batches(self.test2id, self.batch_size)
def main(_):
config = load_config(FLAGS.config)
# Load saved model
print "Loading model"
model_path = os.path.join(config.data.ckpt, 'model.pt')
model = torch.load(model_path)
model.eval()
# Load embeddings and (test) datasets
l1_embeddings, l1_vocab = load_embeddings(path=config.data.l1_embeddings)
l2_embeddings, l2_vocab = load_embeddings(path=config.data.l2_embeddings)
# Translate all test files
start = time.time()
beam_size = 12
test_dirs = ['data/test_en', 'data/test_fr']
for test_dir in test_dirs:
src_lang = test_dir.split('_')[-1]
if src_lang == 'en':
src_lang = 'l1'
src_vocab = l1_vocab
tgt_lang = 'l2'
tgt_vocab = l2_vocab
elif src_lang == 'fr':
src_lang = 'l2'
src_vocab = l2_vocab
tgt_lang = 'l1'
tgt_vocab = l1_vocab
else:
ValueError('source language')
test_dataset = MonolingualDataset(folder=test_dir, vocab=src_vocab)
test_loader = MonolingualDataLoader(test_dataset)
test_file = test_dataset._paths[0].split('/')[2]
print test_file, src_lang
f = open('test_translated/' + test_file + '_translated', 'w')
for i, sample in enumerate(test_loader):
sample = {k: v.cuda() for k, v in sample.items() if v is not None}
src, lengths, _, _ = transform_inputs(
src=sample['src'],
lengths=sample['src_len'],
tgt=sample['src'])
translated = translate(model, src, src_lang, lengths.data, beam_size,
config.data.max_length, tgt_vocab)
f.write(translated+'\n')
f.close()
print("Time to translate file (secs): ", time.time() - start)
def _get_data(config, logger, name):
""" get all the required data for training/testing the classifier """
# load bert-related stuff
bert_models = {'bert':'allenai/scibert_scivocab_uncased',
'roberta' : 'allenai/biomed_roberta_base'}
vocab = bert_models[config['bert_model']]
# load data
partition, training_generator, validation_generator = load_data(config, vocab, max_len = config['max_len'])
# get the embeddings: either from scratch, or from cache
logger.info(f" Getting {config['embedding_type']} embeddings ...")
if (config['embedding_type'] == 'bert') | (config['embedding_type'] == 'roberta'):
embed_shape, train_embeddings, valid_embeddings = load_embeddings(config, name, vocab, training_generator, validation_generator)
elif config['embedding_type'] == 'specter':
# load from filepath
embed_shape, train_embeddings, valid_embeddings = pickle.load(os.path(config["precomputed_embedding_path"]))
else:
raise logger.error("Only BERT, ROBERTA, and Specter embeddings accepted.")
# dimension reduction: PCA (either from scratch, or from cache)
if config["do_pca"]:
logger.info(' Reducing embedding dimensions...')
embed_shape, train_embeddings, valid_embeddings = get_pca_embeddings(config, name, train_embeddings, valid_embeddings)
logger.info(' Dataset is: {} and PCA was performed: {}'.format(name, config["do_pca"]))
logger.info(f'\n Num. training samples: {len(training_generator)} \
\n Num. validation samples: {len(validation_generator)}')
return embed_shape, train_embeddings, valid_embeddings
def load_data(feature_type='identity', embedding_file=None):
# Load graph.
graph = utils.load_graph()
node_ids = list(range(len(graph.nodes)))
# Choose node features from identity, adjacency matrix, or embeddings.
if feature_type == 'identity':
node_features = np.eye(len(graph.nodes))
elif feature_type == 'adjacency':
node_features = nx.to_numpy_matrix(graph, node_ids)
elif feature_type == 'embedding':
embedding_path = 'node2vec/embeddings/' + embedding_file
embeddings = utils.load_embeddings(embedding_path)
node_features = np.array([embeddings[nid] for nid in node_ids])
# Extract graph info to create torch geometric data object.
x = torch.tensor(node_features, dtype=torch.float)
y = torch.tensor(get_labels(graph), dtype=torch.long)
edge_index, edge_attr = get_edges(graph)
data = Data(x=x, edge_index=edge_index, y=y)
# Obtain train/val/test splits.
get_masks(data)
return data
def test_kmedoids(self, emb_filename, res_filename, budget):
print(res_filename)
# stats = ut.graph_stats(self.G, print_stats=False)
v, em = ut.load_embeddings(emb_filename, self.G.nodes())
influenced, influenced_grouped = [], []
seeds = []
for k in range(1, budget + 1):
print('--------', k)
S = ut.get_kmedoids_centers(em, k, v)
I, I_grouped = map_fair_IC((self.G, S))
influenced.append(I)
influenced_grouped.append(I_grouped)
S_g = {
c: []
for c in np.unique(
[self.G.nodes[v]['color'] for v in self.G.nodes])
}
for n in S:
c = self.G.nodes[n]['color']
S_g[c].append(n)
seeds.append(
S_g) # id's of the seeds so the influence can be recreated
ut.write_files(res_filename, influenced, influenced_grouped, seeds)
def read_files(folder, parent):
print("[Local-embedding] Reading file:", parent)
emb_file = '%s/embeddings.txt' % folder
hier_file = '%s/hierarchy.txt' % folder
keyword_file = '%s/keywords.txt' % folder ## here only consider those remaining keywords
embs = utils.load_embeddings(emb_file)
keywords = set()
cates = {}
with open(keyword_file) as f:
for line in f:
keywords.add(line.strip('\r\n'))
tmp_embs = {}
for k in keywords:
if k in embs:
tmp_embs[k] = embs[k]
embs = tmp_embs
with open(hier_file) as f:
for line in f:
segs = line.strip('\r\n').split(' ')
if segs[1] == parent:
cates[segs[0]] = set()
print(
'[Local-embedding] Finish reading embedding, hierarchy and keywords files.'
)
return embs, keywords, cates
def main():
# 在训练集上构建一元和二元词典
word2id = load_word2id(length=VOCAB_SIZE)
# 为深度学习算法准备数据loader
train_loader_dl = DataLoader(
dataset=DianPingDataSet("train"),
batch_size=64,
collate_fn=partial(collate_fn_dl, word2id, SENT_MAX_LEN)
)
test_loader_dl = DataLoader(
dataset=DianPingDataSet("test"),
batch_size=64,
collate_fn=partial(collate_fn_dl, word2id, SENT_MAX_LEN)
)
vocab_size = len(word2id)
print("Vocab Size:", vocab_size)
print("加载词向量....")
try:
embedding = load_embeddings(word2id)
except FileNotFoundError:
embedding = None
# 在深度学习模型上训练测试(CNN, LSTM)
print("在BiLSTM模型上训练...")
lstm_model = DeepModel(vocab_size, embedding, method="lstm")
lstm_model.train_and_eval(train_loader_dl, test_loader_dl)
print("在CNN模型上训练...")
cnn_model = DeepModel(vocab_size, embedding, method="cnn")
cnn_model.train_and_eval(train_loader_dl, test_loader_dl)
def get_related_figures(identifiers_dir, text_data_dir, embeddings_dir,
test_identifiers_dir, output_dir):
"""Get semantically related figures for a set of test figures.
Args:
identifiers_dir: (string) identifiers of all figures in the collection.
text_data_dir: (string) the file with the text for each figure (for keyword retrieval purposes).
embeddings_dir: (string) the embedding vectors for all figures in the collection.
test_identifiers_dir: (string) the figures for which we want to find related figures (a subset of full collection)
output_dir: (string) directory for the output data.
Returns:
None. Outputs the related figures to a file.
"""
test_identifiers = utils.read_lines_from_file(test_identifiers_dir)
all_identifiers = utils.read_lines_from_file(identifiers_dir)
tf_idf_matrix = KnnSearcher.get_tf_idf_embeddings(text_data_dir)
searcher = KnnSearcher(tf_idf_matrix, all_identifiers, 100)
initial_result_list = searcher.perform_keyword_retrieval(test_identifiers)
embedding_matrix = utils.load_embeddings(embeddings_dir)
final_result_list = re_rank_with_embeddings(initial_result_list,
embedding_matrix,
all_identifiers)
with open(output_dir, 'w+') as output_file:
for figure_id in final_result_list:
line = figure_id
for other_figure in final_result_list[figure_id]:
if other_figure[0] != figure_id:
line += ',' + other_figure[0]
output_file.write(line + '\n')
def predict():
source_data,target_data,test_data,word2id=utils.load_data()
embeddings=utils.load_embeddings(word2id)
print "测试集大小 %d" % len(test_data)
results=[]
#HybridCNNSS
g1 = Graph('HybridCNNSS', 'HybridCNNSS1', embeddings)
results.append(g1.run(test_data))
g1 = Graph('HybridCNNSS', 'HybridCNNSS2', embeddings)
results.append(g1.run(test_data))
g1 = Graph('HybridCNNSS', 'HybridCNNSS3', embeddings)
results.append(g1.run(test_data))
g1 = Graph('HybridCNNSS', 'HybridCNNSS4', embeddings)
results.append(g1.run(test_data))
g1 = Graph('HybridCNNSS', 'HybridCNNSS5', embeddings)
results.append(g1.run(test_data))
g1 = Graph('HybridCNNSS', 'HybridCNNSS6', embeddings)
results.append(g1.run(test_data))
g1 = Graph('HybridCNNSS', 'HybridCNNSS7', embeddings)
results.append(g1.run(test_data))
g1 = Graph('HybridCNNSS', 'HybridCNNSS8', embeddings)
results.append(g1.run(test_data))
g1 = Graph('HybridCNNSS', 'HybridCNNSS9', embeddings)
results.append(g1.run(test_data))
g1 = Graph('HybridCNNSS', 'HybridCNNSS10', embeddings)
results.append(g1.run(test_data))
predicts=[]
for predict in np.stack(results,axis=1):
predicts.append(1.0*sum(predict)/len(predict))
utils.generate_file(predicts)
def recursion(root, lvl):
q = Queue.Queue()
q.put((root, -1, 1, '*'))
dbi_scores = {}
while not q.empty():
(c_folder, c_id, level, c_name) = q.get()
if level >= int(lvl):
continue
hier_f = '%s/hierarchy.txt' % c_folder
clus_kws_f = '%s/cluster_keywords.txt' % c_folder
emb_f = '%s/embeddings.txt' % c_folder
if not exists(hier_f):
continue
hier_map = utils.load_hier_f(hier_f)
clus_map = get_clus_keywords(clus_kws_f)
embs = utils.load_embeddings(emb_f)
for cluster in hier_map:
cc_id = hier_map[cluster]
cluster_folder = '%s/%s' % (c_folder, cluster)
cluster_namespace = '%s/%s' % (c_name, cluster)
q.put((cluster_folder, cc_id, level + 1, cluster_namespace))
# handle current
dbi = compute_dbi(embs, clus_map, hier_map)
print 'Computing DBI for %s: %f' % (c_name, dbi)
dbi_scores[c_name] = (dbi, level)
output_dbi(dbi_scores)
def init():
print 'Loading training samples..'
training_samples = utils.load_samples('../data/askubuntu/train_random.txt')
print len(training_samples)
print 'Loading dev samples..'
dev_samples = utils.load_samples('../data/askubuntu/dev.txt')
print len(dev_samples)
print 'Loading test samples..'
test_samples = utils.load_samples('../data/askubuntu/test.txt')
print len(test_samples)
print 'Loading corpus..'
question_map = utils.load_corpus('../data/askubuntu/text_tokenized.txt')
print len(question_map)
print 'Loading stop words..'
stop_words = utils.load_stop_words('../data/english_stop_words.txt')
print len(stop_words)
corpus_texts = map(lambda (t, b): t + ' ' + b, question_map.values())
print 'Loading embeddings..'
embedding_map = utils.load_embeddings(
'../data/pruned_askubuntu_android_vector.txt', corpus_texts,
stop_words)
print len(embedding_map)
print
utils.store_embedding_map(embedding_map)
return (training_samples, dev_samples, test_samples, question_map,
embedding_map)
def predict_image():
"""Gets an image file via POST request, feeds the image to the FaceNet model, the resulting embedding is then
sent to be compared with the embeddings database. The image file is not stored.
An html page is then rendered showing the prediction result.
"""
if request.method == 'POST':
if 'file' not in request.files:
return "No file part"
file = request.files['file']
filename = file.filename
if filename == "":
return "No selected file"
if file and allowed_file(filename=filename, allowed_set=allowed_set):
# Read image file as numpy array of RGB dimension
img = imread(name=file, mode='RGB')
# Detect and crop a 160 x 160 image containing a human face in the image file
img = get_face(img=img,
pnet=pnet,
rnet=rnet,
onet=onet,
image_size=image_size)
# If a human face is detected
if img is not None:
embedding = forward_pass(
img=img,
session=facenet_persistent_session,
images_placeholder=images_placeholder,
embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size)
embedding_dict = load_embeddings()
if embedding_dict:
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(embedding=embedding,
embedding_dict=embedding_dict)
return render_template('predict_result.html',
identity=identity)
else:
return render_template(
'predict_result.html',
identity=
"No embedding files detected! Please upload image files for embedding!"
)
else:
return render_template(
'predict_result.html',
identity=
"Operation was unsuccessful! No human face was detected.")
else:
return "POST HTTP method required!"
def __init__(self, emb_size, vocab_size=11004):
super(Baseline_Embeddings, self).__init__()
self.embedding_prem = nn.Embedding(vocab_size, emb_size)
self.embedding_hypo = nn.Embedding(vocab_size, emb_size)
self.linear = nn.Linear(emb_size * 2, 3)
embeddings_mat = load_embeddings()
self.embedding_prem.weight.data.copy_(embeddings_mat)
self.embedding_hypo.weight.data.copy_(embeddings_mat)
def label_emb_centric(folder, c_id):
print 'Start labeling for %s, %s ========================' % (folder, c_id)
# print folder
par_folder = dirname(folder)
cur_label = basename(folder)
p_case_f = '%s/caseolap.txt' % par_folder
c_case_f = '%s/caseolap.txt' % folder
emb_f = '%s/embeddings.txt' % par_folder
# generate word2vec phrases
embs = utils.load_embeddings(emb_f)
if cur_label not in embs:
print 'Error!!!'
exit(1)
N = 100
worst = -100
bestw = [-100] * (N + 1)
bestp = [''] * (N + 1)
for ph in embs:
sim = utils.cossim(embs[cur_label], embs[ph])
if sim > worst:
for i in range(N):
if sim >= bestw[i]:
for j in range(N - 1, i - 1, -1):
bestw[j + 1] = bestw[j]
bestp[j + 1] = bestp[j]
bestw[i] = sim
bestp[i] = ph
worst = bestw[N - 1]
break
cands = [(bestp[idx], bestw[idx]) for idx, x in enumerate(bestp)]
phrase_map_p, cell_map_p, tmp = read_caseolap_result(p_case_f)
parent_dist_ranking = cell_map_p[c_id]
parent_dist_map = {ph: float(dist) for (ph, dist) in parent_dist_ranking}
child_kl_ranking = rank_phrase(c_case_f)
child_kl_map = {ph: dist for (ph, dist) in child_kl_ranking}
min_score = 0.12
label_cands = {}
# for (ph, score) in parent_dist_ranking:
for (ph, score) in cands:
if ph not in parent_dist_map:
continue
if ph in child_kl_map:
continue
label_cands[ph] = score
ranked_list = sorted(label_cands.items(),
key=operator.itemgetter(1),
reverse=True)
print ranked_list
return ranked_list[0][0]
def get_frame(self):
success, frame_orig = self.video.read()
# We are using Motion JPEG, but OpenCV defaults to capture raw images,
# so we must encode it into JPEG in order to correctly display the
# video stream.
# faces = face_cascade.detectMultiScale(image, 1.3, 5)
frame = cv2.resize(src=frame_orig, dsize=(0, 0), fx=0.5, fy=0.5)
embedding_dict = load_embeddings()
frame = frame[:, :, ::-1]
if frame.size > 0:
faces, rects = get_faces_live(img=frame,
pnet=pnet,
rnet=rnet,
onet=onet,
image_size=image_size)
# If there are human faces detected
if faces:
for i in range(len(faces)):
face_img = faces[i]
rect = rects[i]
# Scale coordinates of face locations by the resize ratio
rect = [coordinate * 2 for coordinate in rect]
face_embedding = forward_pass(
img=face_img,
session=facenet_persistent_session,
images_placeholder=images_placeholder,
embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size)
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(embedding=face_embedding,
embedding_dict=embedding_dict)
cv2.rectangle(img=frame_orig,
pt1=(rect[0], rect[1]),
pt2=(rect[2], rect[3]),
color=(255, 215, 0),
thickness=2)
W = int(rect[2] - rect[0]) // 2
cv2.putText(img=frame_orig,
text=identity,
org=(rect[0] + W - (W // 2), rect[1] - 7),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=(255, 215, 0),
thickness=1,
lineType=cv2.LINE_AA)
ret, jpeg = cv2.imencode('.jpg', frame_orig)
return jpeg.tobytes()
def main(analysis=False):
# Load mappings and embeddings for specified network(s)
mappings = ut.load_mappings()
embeddings = ut.load_embeddings()
# Extract IIDs for specified seed ingredients
ingredient_to_iid = {
ingredient: iid
for iid, ingredient in
mappings['IID_to_Ingredient_Mapping'].iteritems()
}
if args.seed_ingredients is not None:
args.seed_ingredients = [
ingredient_to_iid[ingredient]
for ingredient in args.seed_ingredients
]
if args.accent > 0 and not args.network == 'ocn_fph':
raise Exception(
'You set accent > 1 but did not use network \'ocn_fph\'.')
num_ingredients = np.random.randint(args.min, args.max + 1)
if args.accent > args.min:
raise Exception(
'Number of accent ingredients cannot be greater than the minimum number of ingredients.'
)
if args.cuisine == 'random':
cuisine = np.random.choice(
mappings['Cuisine_to_List_of_Ingredients_Mapping'].keys())
print 'Randomly Chosen Cuisine: {}'.format(cuisine)
else:
cuisine = args.cuisine
if args.network == 'ocn_fph':
recipe = base_accent_generate(get_embeddings(embeddings, 'ocn', mappings, cuisine), \
get_embeddings(embeddings, 'fph', mappings, cuisine), args.seed_ingredients, num_ingredients, args.accent)
elif args.network == 'ucn':
recipe = generate(get_embeddings(embeddings, 'ucn', mappings, cuisine),
args.seed_ingredients, num_ingredients)
else:
raise NotImplementedError
if args.avoids is not None:
avoid_iids = [ingredient_to_iid[a] for a in args.avoids]
SN, SW = ut.load_sn()
recipe = substitute_avoids(SW, \
get_embeddings(embeddings, 'ocn', mappings, cuisine), avoid_iids, recipe)
if analysis:
return recipe
base_ingredients = num_ingredients - args.accent
for i, iid in enumerate(recipe):
if args.network == 'ocn_fph' and i >= base_ingredients:
print mappings['IID_to_Ingredient_Mapping'][iid], '(accent)'
else:
print mappings['IID_to_Ingredient_Mapping'][iid]
def __init__(self,
pickle_path,
eval_path,
encoding="utf8"):
self.eval_path = eval_path
self.encoding = encoding
self.embeddings, self.word2index = load_embeddings(pickle_path)
self.top_results = min(self.embeddings.shape[0] - 2, 10)
def load_all_embeddings(self, word_index, num_words):
# Word cover rate in the embedding is: 0.8724167059563099
glove_embeddings = load_embeddings(GLOVE_PATH, word_index, num_words)
# Word cover rate in the embedding is: 0.6717114568599717
wiki_embeddings = load_embeddings(WIKI_PATH, word_index, num_words)
# google_new_embeddings = load_embeddings(GOOGLE_NEWS_PATH, word_index, num_words)
# paragram_embeddings = load_embeddings(PARAGRAM_PATH, word_index, num_words)
embedding_matrix = np.concatenate(
(
glove_embeddings,
wiki_embeddings,
# google_new_embeddings,
# paragram_embeddings,
),
axis=1)
return torch.tensor(glove_embeddings, dtype=torch.float32)
def find_every_words_not_in_embeddings(embedding_path, vocab):
oov = set()
embeddings = load_embeddings(embedding_path)
for token in vocab:
if token not in embeddings and token.capitalize(
) not in embeddings and token.upper(
) not in embeddings and token.lower() not in embeddings:
oov.add(token)
return oov
def init():
print 'Loading askubuntu training samples..'
askubuntu_training_samples = utils.load_samples(
'../data/askubuntu/train_random.txt')
print len(askubuntu_training_samples)
print 'Loading askubuntu dev samples..'
askubuntu_dev_samples = utils.load_samples('../data/askubuntu/dev.txt')
print len(askubuntu_dev_samples)
print 'Loading askubuntu test samples..'
askubuntu_test_samples = utils.load_samples('../data/askubuntu/test.txt')
print len(askubuntu_test_samples)
print 'Loading askubuntu corpus..'
askubuntu_question_map = utils.load_corpus(
'../data/askubuntu/text_tokenized.txt')
print len(askubuntu_question_map)
print 'Loading android dev samples..'
android_dev_samples = utils.load_samples_stupid_format(
'../data/android/dev.pos.txt', '../data/android/dev.neg.txt')
print len(android_dev_samples)
print 'Loading android test samples..'
android_test_samples = utils.load_samples_stupid_format(
'../data/android/test.pos.txt', '../data/android/test.neg.txt')
print len(android_test_samples)
print 'Loading android corpus..'
android_question_map = utils.load_corpus('../data/android/corpus.tsv')
print len(android_question_map)
print 'Loading stop words..'
stop_words = utils.load_stop_words('../data/english_stop_words.txt')
print len(stop_words)
corpus_texts = map(lambda (t, b): t + ' ' + b,
askubuntu_question_map.values() + android_question_map.values())
print 'Loading embeddings..'
embedding_map = utils.load_embeddings(
'../data/pruned_android_vector.txt', corpus_texts, stop_words) # pruned_askubuntu_android_vector.txt
print len(embedding_map)
print
utils.store_embedding_map(embedding_map)
return (
askubuntu_training_samples,
askubuntu_dev_samples,
askubuntu_test_samples,
askubuntu_question_map,
android_dev_samples,
android_test_samples,
android_question_map,
embedding_map)
def train_network(vectorizer, network_type, task_type, train_table,
setting_name):
"""
Main function of vectorization for neural network
network_type : str
type of the network, which should be presented in NETWORKS dictionary.
task_type : str
TTK_TASK or BANK_TASK
train_table : str
Train table filepath
returns : None
"""
message_settings, features_settings = utils.load_embeddings()
features = Features(
TwitterMessageParser(message_settings, task_type),
features_settings)
term_vocabulary = TermVocabulary()
doc_vocabulary = DocVocabulary()
problem = utils.create_problem(task_type,
'train',
train_table,
vectorizer,
features,
term_vocabulary,
doc_vocabulary,
message_settings)
assert(len(problem) > 0)
X, y = get_problem(problem, get_results=True)
embedding_size = X.shape[1]
logging.info("embedding_size: {}".format(embedding_size))
logging.info("Create RNN network model ...")
# TODO:
# Network setting should be presented in json configuration (apperently
# rnn.conf)
hidden_size = 400
model = get_network(network_type, embedding_size, hidden_size)
paths = get_model_paths(task_type, network_type, setting_name)
logging.info("Pack embedding settings: {} ...".format(
paths['embedding_output']))
save_embeddings(paths['embedding_output'])
logging.info("Save term vocabulary: {} ...".format(
paths['term_vocabulary']))
term_vocabulary.save(paths['term_vocabulary'])
optimizer.train_network(model, X, y, paths['model_output'])
def __init__(self, csvpath, mode='train'):
self.mode = mode
df = pd.read_csv(csvpath)
le = LabelEncoder()
if self.mode == 'train':
'''
Load the train data and split into train test sets
'''
X = list(df['text'])
'''
tokenize the input X_train data
'''
tok = keras.preprocessing.text.Tokenizer(num_words=1000)
tok.fit_on_texts(X)
if self.mode == 'train':
self.tok = tok
# integer encode documents
X_train = tok.texts_to_sequences(X)
#padd so all same length
X_train = keras.preprocessing.sequence.pad_sequences(
X_train, padding='post')
self.maxpad = X_train.shape[1]
self.inp = self.X_train.values
self.oup = self.list(df['target'])
'''
Load word embeddings
'''
word_counts = pd.DataFrame(
dict(tok.word_counts),
index=['count']).transpose().sort_values(by='count',
ascending=False)
num_words = len(word_counts)
tok_dict = dict(tok.index_word)
word_embeddings_dict = utils.load_embeddings(
'./data/non_tracked/glove.6B.100d.txt')
'''
Create the embedding_matrix for the words in our vocabulary
'''
embeddings_words = list(word_embeddings_dict.keys())
wordvec_dim = word_embeddings_dict[
embeddings_words[0]].shape[0]
embedding_matrix = np.zeros((num_words, wordvec_dim))
for i, word in tok_dict.items():
# Look up the word embedding
vector = word_embeddings_dict.get(word, None)
# Record in matrix
if vector is not None:
embedding_matrix[i, :] = vector
self.embedding_matrix = embedding_matrix
else:
#transform test data
X_test = self.tok.texts_to_sequences(X)
X_test = keras.preprocessing.sequence.pad_sequences(
X_test, padding='post', maxlen=self.maxpad)
self.inp = self.X_test.values
def create_oov(dataset, embeddings_path):
sentences = dataset.get_train_sentences + dataset.get_valid_sentences + dataset.get_test_sentences
embeddings = load_embeddings(embeddings_path)
oov = sorted(set(word for sentence in sentences for word in sentence if word not in embeddings))
filepath = './' + dataset.dataset_name + '/oov.txt'
with open(filepath, 'w', encoding='utf-8') as file:
file.write('\n'.join(oov)+'\n')
def main():
print("Loading embeddings")
#load embeddings
if os.name == "nt":
# embd_dict = utils.load_embeddings(
# "C:/Users/anlausch/workspace/cnn-text-classification/data/GoogleNews-vectors-negative300.bin", word2vec=True)
embd_dict = utils.load_embeddings(
"C:/Users/anlausch/workspace/embedding_files/glove.6B/glove.6B.50d.txt",
word2vec=False)
else:
# embd_dict = utils.load_embeddings("~/GoogleNews-vectors-negative300.bin", word2vec=True)
embd_dict = utils.load_embeddings("./glove.6B.300d.txt",
word2vec=False)
# print("Grid Search with SVM for TFIDF Embedding Features")
# print("===========================================")
# for task in ["discourse", "aspect", "summary"]:
# #grid_search_linear_svm_tfidf(task=task)
# grid_search_rbf_svm_tfidf_embeddings(embd_dict=embd_dict, task=task)
#
# print("Grid Search with SVM for Embedding Features")
# print("===========================================")
# for task in ["discourse", "aspect", "summary"]:
# #grid_search_linear_svm_tfidf(task=task)
# grid_search_rbf_svm_embeddings(embd_dict=embd_dict, task=task)
print("Grid Search with SVM for TFIDF")
print("===========================================")
for task in ["discourse", "aspect", "summary"]:
#grid_search_linear_svm_tfidf(task=task)
grid_search_rbf_svm_tfidf(task=task)
print("Grid Search with SVM linear embeddings")
print("===========================================")
for task in ["discourse", "aspect", "summary"]:
#grid_search_linear_svm_tfidf(task=task)
grid_search_linear_svm_embeddings(embd_dict=embd_dict, task=task)
print("Grid Search with SVM linear for TFIDF")
print("===========================================")
for task in ["discourse", "aspect", "summary"]:
#grid_search_linear_svm_tfidf(task=task)
grid_search_linear_svm_tfidf(task=task)
def load_embedding_dict(vocab_path="",
vector_path="",
embeddings_path="",
glove=False,
postspec=False):
"""
>>> _load_embedding_dict()
:param vocab_path:
:param vector_path:
:return: embd_dict
"""
if glove and postspec:
raise ValueError("Glove and postspec cannot both be true")
elif glove:
if os.name == "nt":
embd_dict = utils.load_embeddings(
"C:/Users/anlausch/workspace/embedding_files/glove.6B/glove.6B.300d.txt",
word2vec=False)
else:
embd_dict = utils.load_embeddings(
"/work/anlausch/glove.6B.300d.txt", word2vec=False)
return embd_dict
elif postspec:
embd_dict_temp = utils.load_embeddings(
"/work/anlausch/ft_postspec.txt", word2vec=False)
embd_dict = {}
for key, value in embd_dict_temp.items():
embd_dict[key.split("en_")[1]] = value
assert ("test" in embd_dict)
assert ("house" in embd_dict)
return embd_dict
elif embeddings_path != "":
embd_dict = utils.load_embeddings(embeddings_path, word2vec=True)
return embd_dict
else:
embd_dict = {}
vocab = load_vocab_goran(vocab_path)
vectors = load_vectors_goran(vector_path)
for term, index in vocab.items():
embd_dict[term] = vectors[index]
assert len(embd_dict) == len(vocab)
return embd_dict
def __init__(self, task):
self.ckpt_path = './ckpt/{}/'.format(task)
if not os.path.exists(self.ckpt_path):
os.makedirs(self.ckpt_path)
source_dir = os.path.join('.', 'dataset', 'data', task)
self.word_vocab, _ = load_vocab(os.path.join(source_dir, 'words.vocab'))
self.char_vocab, _ = load_vocab(os.path.join(source_dir, 'chars.vocab'))
self.vocab_size = len(self.word_vocab)
self.char_vocab_size = len(self.char_vocab)
self.label_size = load_json(os.path.join(source_dir, 'label.json'))["label_size"]
self.word_emb = load_embeddings(os.path.join(source_dir, 'glove.filtered.npz'))
def predict_image(file):
# file = request.files['file']
# file = os.path.join(APP_ROOT, 'uploads/Abdulrahman Safh.png')
# Read image file as numpy array of RGB dimension
#img = io.imread(fname=file)
img = imread(name=file, mode='RGB')
# Detect and crop a 160 x 160 image containing a human face in the image file
faces, rects = get_faces_live(img=img, pnet=pnet, rnet=rnet,
onet=onet, image_size=image_size)
#global d
# If there are human faces detected
if faces:
embedding_dict = load_embeddings()
if embedding_dict:
people_found = []
for i in range(len(faces)):
face_img = faces[i]
rect = rects[i]
face_embedding = forward_pass(
img=face_img, session=facenet_persistent_session,
images_placeholder=images_placeholder, embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size
)
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(
embedding=face_embedding, embedding_dict=embedding_dict)
people_found.append(identity)
cv2.rectangle(img, (rect[0], rect[1]), (rect[2], rect[3]), (0, 255, 0), 3)
W = int(rect[2] - rect[0]) // 2
H = int(rect[3] - rect[1]) // 2
cv2.putText(img, identity, (rect[0] + W - (W // 2), rect[1] - 7),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 1, cv2.LINE_AA)
# code for saving the output images
# cv2.imwrite("SavedImgesFull/file_%d.jpg" % d, img)
#d += 1
return people_found
else:
# return ["No Face"]
return None
# return render_template(
# 'predict_result.html',
# identity="No embedding files detected! Please upload image files for embedding!"
# )
else:
# return ["No Image"]
return None
def __init__(self):
# Logger
self.logger = logger_init()
# Use Cuda
Config.cuda = True
self.device = None
if Config.cuda and torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
################## Data ###################
# Load Sparse Adjacency Matrix
file_name = 'adj_input.pkl'
(data, rows, columns, vocab_dict) = pd.read_pickle(file_name)
id_word_map = {v: k for k, v in vocab_dict.items()}
rel_list = ['ISA']
num_entities = len(vocab_dict)
num_relations = len(rel_list)
# Build the adjacency matrix and remove the edges which fre < 10.
rows = rows + [i for i in range(num_entities)]
columns = columns + [i for i in range(num_entities)]
data = data + [1 for i in range(num_entities)]
adjs = coo_matrix((data, (rows, columns)),
shape=(num_entities, num_entities)).toarray()
# only hyponym-hypernym candidate pairs observed more than 10 times are used to create a noisy graph.
adjs = np.where(adjs >= 10, 1, 0)
self.adjs = torch.FloatTensor(adjs).to(device=self.device)
del rows
del columns
del data
# Use X as index for the randomly initialized embeddings
self.X = torch.LongTensor([i for i in range(num_entities)
]).to(device=self.device)
# Load the word embedding if we use it.
self.word_embs = load_embeddings(vocab_dict).to(device=self.device)
logging.info('Finished the preprocessing')
################## Model, Optimizer, LossFunction ###################
self.model = GRAPH2TAXO(num_entities,
num_relations).to(device=self.device)
self.opt = torch.optim.Adam(self.model.parameters(),
lr=Config.learning_rate,
weight_decay=Config.L2)
self.f1_loss = F1_Loss().to(device=self.device)
################## Part of Hyperparameters ###################
# Hyperparameters for the constraints
self.lambda_A = 1.0 # 1.0
self.c_A = 0.5 # 0.5
self.tau_A = 1.0 # 1.0
def train(preproc_dir, n_classes, max_length, hidden_units, dropout,
batch_size, epochs, output_dir):
"""
Train the ESIM model on some dataset and save the learned weights.
Args:
preproc_dir: The directory where the preprocessed data is saved.
n_classes: The number of classes in the problem.
max_length: The maximum length of the sentences in the premises and
hypotheses of the dataset.
hidden_units: The number of hidden units to use in the various layers
of the model.
dropout: The dropout rate to use in the model.
batch_size: The size of the batches to use for training.
epochs: The number of epochs to apply during training.
output_dir: The path to the directory where the weights learned during
training must be saved.
"""
print("Loading training and validation data...")
train_premises, train_hyps, train_labels = prepare_data(
preproc_dir, 'train', n_classes, max_length)
valid_premises, valid_hyps, valid_labels = prepare_data(
preproc_dir, 'dev', n_classes, max_length)
# train_premises是如下形式：
# [[5, 6, 7, 8, 9, 3, 10, 11, 12, 13, 14, 2, 15, 16, 3，0，0，0，0],
# [17, 18, 19, 20, 21, 22, 4, 23, 2, 24，0，0，0，0，0，0，0，0，0],
# [25, 26, 27，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0，0]]
print("Loading embedding weights...")
embedding_weights = load_embeddings(
os.path.join(preproc_dir, "embedding_weights.pkl"))
# Build the model.
esim = ESIM(n_classes, embedding_weights, max_length, hidden_units,
dropout)
model = esim.build_model()
if not os.path.exists(output_dir):
os.makedirs(output_dir)
filepath = os.path.join(output_dir,
"weights-{epoch:02d}-{val_acc:.2f}.hdf5")
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
model.fit(x=[train_premises, train_hyps],
y=train_labels,
batch_size=batch_size,
epochs=epochs,
validation_data=([valid_premises, valid_hyps], valid_labels),
callbacks=[checkpoint],
shuffle=True)
def prepare_problem(vectorizer, task_type, train_table, test_table,
etalon_table):
"""
Main function of vectorization for neural network
"""
message_settings, features_settings = utils.load_embeddings()
features = Features(
TwitterMessageParser(message_settings, task_type),
features_settings)
term_vocabulary = TermVocabulary()
doc_vocabulary = DocVocabulary()
train_problem = utils.create_problem(task_type, 'train', train_table,
vectorizer, features, term_vocabulary,
doc_vocabulary, message_settings)
test_problem = utils.create_problem(task_type, 'test', test_table,
vectorizer, features, term_vocabulary,
doc_vocabulary, message_settings)
return (train_problem, test_problem)
cwd = os.getcwd()
vectorizer = Vectorizer(min_frequency=config.min_freq)
validation_data_path = cwd + config.relative_dev_path
validation_abstracts = headline2abstractdataset(validation_data_path, vectorizer, args.cuda, max_len=1000)
data_path = cwd + config.relative_data_path
abstracts = headline2abstractdataset(data_path, vectorizer, args.cuda, max_len=1000)
print("number of training examples: %d" % len(abstracts))
vocab_size = abstracts.vectorizer.vocabulary_size
embedding = nn.Embedding(vocab_size, config.emsize, padding_idx=0)
if config.pretrained:
embedding = load_embeddings(embedding, abstracts.vectorizer.word2idx, config.pretrained, config.emsize)
context_encoder = ContextEncoder(config.context_dim, len(abstracts.context_vectorizer), config.emsize)
encoder_title = EncoderRNN(vocab_size, embedding, abstracts.head_len, config.emsize, input_dropout_p=config.dropout,
n_layers=config.nlayers, bidirectional=config.bidirectional, rnn_cell=config.cell)
encoder = EncoderRNN(vocab_size, embedding, abstracts.abs_len, config.emsize, input_dropout_p=config.dropout, variable_lengths = False,
n_layers=config.nlayers, bidirectional=config.bidirectional, rnn_cell=config.cell)
decoder = DecoderRNNFB(vocab_size, embedding, abstracts.abs_len, config.emsize, sos_id=2, eos_id=1,
n_layers=config.nlayers, rnn_cell=config.cell, bidirectional=config.bidirectional,
input_dropout_p=config.dropout, dropout_p=config.dropout)
model = FbSeq2seq(encoder_title, encoder, context_encoder, decoder)
total_params = sum(x.size()[0] * x.size()[1] if len(x.size()) > 1 else x.size()[0] for x in model.parameters())
print('Model total parameters:', total_params, flush=True)
if config.dataparallel and torch.cuda.device_count() > 1:
def train_network(vectorizer, network_type, task_type, train_table,
test_table, etalon_table, setting_name):
"""
Main function of vectorization for neural network
"""
message_settings, features_settings = utils.load_embeddings()
features = Features(
TwitterMessageParser(message_settings, task_type),
features_settings)
term_vocabulary = TermVocabulary()
doc_vocabulary = DocVocabulary()
train_problem = utils.create_problem(task_type, 'train', train_table,
vectorizer, features, term_vocabulary,
doc_vocabulary, message_settings)
test_problem = utils.create_problem(task_type, 'test', test_table,
vectorizer, features, term_vocabulary,
doc_vocabulary, message_settings)
assert(len(train_problem) > 0 and len(test_problem) > 0)
# Transform into appliable for neural network collections
X_test = get_problem(test_problem, get_results=False)
X_train, Y = get_problem(train_problem, get_results=True)
assert(X_test.shape[1] == X_train.shape[1])
embedding_size = X_test.shape[1]
logging.info("embedding_size: {}".format(embedding_size))
logging.info("Create {} network model ...".format(network_type))
# TODO:
# Network setting should be presented in json configuration (apperently
# rnn.conf)
hidden_layer_size = 400
model = get_network(network_type, embedding_size, hidden_layer_size)
paths = get_model_paths(task_type, network_type, setting_name)
diagnostic_output = join(configs.NETWORK_MODELS_ROOT,
"{}.diag".format(setting_name))
logging.info("Pack embedding settings: {} ...".format(
paths['embedding_output']))
save_embeddings(paths['embedding_output'])
def callback(model, X_test, X_train, Y, task_type, result_table,
etalon_table, diagnostic_output):
"""
Test model
"""
logging.info("Testing model ...")
loss = model.calculate_loss(X_train, Y)
predict(model, X_test, task_type, result_table)
result = check(task_type, result_table, etalon_table)
logging.info("Appending results: {} ...".format(diagnostic_output))
with open(diagnostic_output, 'a') as output:
output.writelines("{} {} {}\n".format(
loss, result["F_macro"], result["F_micro"]))
model_output = paths['model_output']
if (exists(model_output)):
logging.info("Loading existed model: {} ...".format(model_output))
model.load(model_output)
output_table = test_table + '.result.csv'
prepare_result_table(test_table, output_table)
test = lambda: callback(model, X_test, X_train, Y, task_type,
output_table, etalon_table, diagnostic_output)
optimizer.train_network(model, X_train, Y, model_output, callback=test)
with open(diagnostic_output, 'a') as output:
output.writelines("-----")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
# Data Preparatopn
# ==================================================
# Load data
print("Loading data...")
vocab, embeddings = utils.load_embeddings()
train_data = utils.load_train_data(vocab, FLAGS.sequence_length)
test_data = utils.load_test_data(vocab, FLAGS.sequence_length)
print("Load done...")
# Training
# ==================================================
prev_auc = 0
with tf.Graph().as_default():
with tf.device("/gpu:1"):
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():