def social_context_dataset_statistics():
"""
Utility method to perform social context (PHEME dataset structure based) corpus statistics by a given path
:return:
"""
# social_context_data_dir = "C:\\Data\\NLP-corpus\\aug_rnr\\twitter1516"
from data_loader import load_abs_path
social_context_data_dir = os.path.join(
os.path.dirname(__file__), '..', "data", "social_context",
"aug-rnr-annotated-threads-retweets")
social_context_data_dir = load_abs_path(social_context_data_dir)
print("check social context corpus [%s] ... " % social_context_data_dir)
events_dataset_dirs = []
for root, dirs, files in os.walk(social_context_data_dir):
# print("root: ", root)
# print("dirs: ", dirs)
# print("files size: ", len(files))
events_dataset_dirs = dirs
break
print("total [%s] events dataset" % len(events_dataset_dirs))
print(events_dataset_dirs)
# check every individual event corpus
for event_dataset_dir in events_dataset_dirs:
labelled_event_dataset_statistics(social_context_data_dir,
event_dataset_dir)
print(" ========================================== ")
print("complete.")
def test_context_sequence_encoding(self):
elmo_credbank_model_path = load_abs_path(
os.path.join(
os.path.dirname(__file__), '..', "resource", "embedding",
"elmo_model",
"elmo_credbank_2x4096_512_2048cnn_2xhighway_weights_10052019.hdf5"
))
elmo_embedder = ElmoTokenEmbedder(
options_file=
"https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json",
weight_file=elmo_credbank_model_path,
do_layer_norm=False,
dropout=0.5)
word_embeddings = BasicTextFieldEmbedder({"tokens": elmo_embedder})
EXPECTED_CONTEXT_INPUT_SIZE = 60
rumor_classifier = RumorTweetsClassifer(
word_embeddings,
None,
None,
None,
classifier_feedforward=None,
cxt_content_encoder=None,
cxt_metadata_encoder=None,
social_context_self_attention_encoder=None,
cuda_device=-1)
tweet_id = "500327120770301952"
single_source_tweet_tensor_1 = self.tweet_context_encoding_by_tweet_id(
rumor_classifier, tweet_id)
print(type(single_source_tweet_tensor_1))
print(single_source_tweet_tensor_1.shape)
assert type(single_source_tweet_tensor_1) == torch.Tensor
assert single_source_tweet_tensor_1.shape == (
97, EXPECTED_CONTEXT_INPUT_SIZE
), "expected shape is [19, %s]" % EXPECTED_CONTEXT_INPUT_SIZE
tweet_id = "552806117328568321" # with three replies
single_source_tweet_tensor_2 = self.tweet_context_encoding_by_tweet_id(
rumor_classifier, tweet_id)
print(type(single_source_tweet_tensor_2))
print(single_source_tweet_tensor_2.shape)
assert type(single_source_tweet_tensor_2) == torch.Tensor
assert single_source_tweet_tensor_2.shape == (
94, EXPECTED_CONTEXT_INPUT_SIZE
), "expected shape is [3, %s]" % EXPECTED_CONTEXT_INPUT_SIZE
tweet_id = "552806117328568321" # with three replies
print("social context encoding without numerical feature .")
single_source_tweet_tensor_2 = self.tweet_context_encoding_by_tweet_id(
rumor_classifier, tweet_id, disable_nf=True)
print(type(single_source_tweet_tensor_2))
print(single_source_tweet_tensor_2.shape)
assert type(single_source_tweet_tensor_2) == torch.Tensor
assert single_source_tweet_tensor_2.shape == (
94, EXPECTED_CONTEXT_INPUT_SIZE
), "expected shape is [3, %s]" % EXPECTED_CONTEXT_INPUT_SIZE
def test_elmo_with_attention():
import os
from data_loader import load_abs_path
from embedding_layer import word_embedding_elmo
elmo_credbank_model_path = load_abs_path(
os.path.join(
os.path.dirname(__file__), '..', "resource", "embedding",
"elmo_model",
"elmo_credbank_2x4096_512_2048cnn_2xhighway_weights_10052019.hdf5")
)
elmo_options_file_path = load_abs_path(
os.path.join(os.path.dirname(__file__), '..', "resource", "embedding",
"elmo_model",
"elmo_2x4096_512_2048cnn_2xhighway_options.json"))
sentence3 = [
"9/11", "sandy", "hook", "movie", "shooting", "boston", "bomb",
"threats", "from", "n.", "korea", "and", "several", "other",
"tragedies", "were", "all", "under", "the", "age", "of", "18"
]
fine_tuned_elmo = ElmoEmbedder(options_file=elmo_options_file_path,
weight_file=elmo_credbank_model_path)
avg_all_layer_sent_embedding = word_embedding_elmo(sentence3,
fine_tuned_elmo)
# print(avg_all_layer_sent_embedding)
print("content avg ELMo embedding shape : ",
avg_all_layer_sent_embedding.shape)
assert avg_all_layer_sent_embedding.shape == (22, 1024)
attention_layer = HierarchicalAttentionNet(1024, step_dim=22)
maxlen = 200
attention_weights = attention_layer.forward(
torch.as_tensor(avg_all_layer_sent_embedding), maxlen)
print("context attention weights shape: ", attention_weights.shape)
assert attention_weights.shape == torch.Size([1, 1024])
print(attention_weights)
def test_context_feature_encoder(self):
elmo_credbank_model_path = load_abs_path(
os.path.join(
os.path.dirname(__file__), '..', "resource", "embedding",
"elmo_model",
"elmo_credbank_2x4096_512_2048cnn_2xhighway_weights_10052019.hdf5"
))
# test context feature encoding with small sample data
# to make sure that source tweet context are sorted in temporal order
elmo_embedder = ElmoTokenEmbedder(
options_file=
"https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json",
weight_file=elmo_credbank_model_path,
do_layer_norm=False,
dropout=0.5)
word_embeddings = BasicTextFieldEmbedder({"tokens": elmo_embedder})
rumor_classifier = RumorTweetsClassifer(word_embeddings, None, None,
None, None)
propagation_embeddings_tensor = rumor_classifier.batch_compute_context_feature_encoding(
['500294803402137600', '500327120770301952'])
print("propagation_embeddings_tensor: ", propagation_embeddings_tensor)
"1) AttentionWithContext (default). However, we got [%s]" %
attention_option)
print(
"training RumourDNN model on development dataset [%s] and [%s] with gpu [%s]"
% (train_set_path, heldout_set_path, no_gpu))
import allennlp_rumor_classifier
import data_loader
from allennlp_rumor_classifier import config_gpu_use
config_gpu_use(no_gpu)
allennlp_rumor_classifier.elmo_credbank_model_path = load_abs_path(
os.path.join(
os.path.dirname(__file__), '..', "resource", "embedding",
"elmo_model",
"elmo_credbank_2x4096_512_2048cnn_2xhighway_weights_10052019.hdf5")
)
data_loader.social_context_data_dir = os.path.join(
os.path.dirname(__file__), '..', "data", "social_context",
"aug-rnr-annotated-threads-retweets")
print("Fine-tuned ELMo model is set to [%s]" %
allennlp_rumor_classifier.elmo_credbank_model_path)
print("social context corpus for all events directory is set to [%s]" %
data_loader.social_context_data_dir)
# Reasonable minibatch sizes are usually: 32, 64, 128, 256, 512, 1024 (powers of 2 are a common convention)
# Usually, you can choose a batch size that is as large as your GPU memory allows
# (matrix-multiplication and the size of fully-connected layers are usually the bottleneck)
def statistics_rumour_dnn_dataset(file_name):
"""
perform statistics of social context for a given training data set file
:param file_name:
:return:
"""
print("statistics of [%s]" % file_name)
df_file = load_matrix_from_csv(file_name, 0, 1, header=0)
#for dataset_row in df_file[:]:
# print("tweet id: [%s]" % dataset_row[0])
all_tweet_ids = [dataset_row[0] for dataset_row in df_file[:]]
print("all_tweet_ids size: ", len(all_tweet_ids))
from data_loader import load_tweets_context_dataset_dir
from data_loader import load_abs_path
social_context_data_dir = os.path.join(
os.path.dirname(__file__), '..', "data", "social_context",
"aug-rnr-annotated-threads-retweets")
social_context_data_dir = load_abs_path(social_context_data_dir)
context_tweets_dataset_dir_dict = load_tweets_context_dataset_dir(
social_context_data_dir)
all_replies_list = []
all_retweets_list = []
for tweet_id in all_tweet_ids:
total_replies, total_retweets = count_social_context(
str(tweet_id), context_tweets_dataset_dir_dict)
all_replies_list.append(total_replies)
all_retweets_list.append(total_retweets)
print("total_replies_list: ", all_replies_list)
print("total_retweets_list: ", all_retweets_list)
total_reactions = sum(all_replies_list)
min_reactions = 0 if len(all_replies_list) == 0 else min(all_replies_list)
max_reactions = 0 if len(all_replies_list) == 0 else max(all_replies_list)
avg_reactions = 0 if len(all_replies_list) == 0 else round(
sum(all_replies_list) / len(all_replies_list), 1)
std_reactions = 0 if len(all_replies_list) == 0 else statistics.stdev(
all_replies_list)
# Median has a very big advantage over Mean, which is the median value is not skewed so much by extremely large or small values.
# see also https://www.geeksforgeeks.org/python-statistics-median/
median_reactions = 0 if len(all_replies_list) == 0 else statistics.median(
all_replies_list)
total_retweets = sum(all_retweets_list)
min_retweets = 0 if len(all_retweets_list) == 0 else min(all_retweets_list)
max_retweets = 0 if len(all_retweets_list) == 0 else max(all_retweets_list)
avg_retweets = 0 if len(all_retweets_list) == 0 else round(
total_retweets / len(all_retweets_list), 1)
std_retweets = 0 if len(all_retweets_list) == 0 else statistics.stdev(
all_retweets_list)
# Median has a very big advantage over Mean, which is the median value is not skewed so much by extremely large or small values,
# see also https://www.geeksforgeeks.org/python-statistics-median/
median_retweets = 0 if len(all_retweets_list) == 0 else statistics.median(
all_retweets_list)
print(
"total reactions: [%s], min reaction: [%s], max reaction: [%s], avg reaction: [%s], std reactions: [%s], median reactions: [%s]"
% (total_reactions, min_reactions, max_reactions, avg_reactions,
std_reactions, median_reactions))
print(
"total retweets: [%s], min retweets: [%s], max retweets: [%s], avg retweets: [%s], std retweets: [%s], median retweets: [%s]"
% (total_retweets, min_retweets, max_retweets, avg_retweets,
std_retweets, median_retweets))
print("total tweets without reaction: [%s]" % (all_replies_list.count(0)))
print("total tweets without retweets: [%s]" % (all_retweets_list.count(0)))
results = dict()
results["total_reactions"] = total_reactions
results["min_reactions"] = min_reactions
results["max_reactions"] = max_reactions
results["avg_reactions"] = avg_reactions
results["std_reactions"] = std_reactions
results["median_reactions"] = median_reactions
results["total_retweets"] = total_retweets
results["min_retweets"] = min_retweets
results["max_retweets"] = max_retweets
results["avg_retweets"] = avg_retweets
results["std_retweets"] = std_retweets
results["median_retweets"] = median_retweets
print("statistics: ")
print(results)
def test_elmo_output_with_self_attention():
import os
from data_loader import load_abs_path
import numpy as np
elmo_credbank_model_path = load_abs_path(
os.path.join(
os.path.dirname(__file__), '..', "resource", "embedding",
"elmo_model",
"elmo_credbank_2x4096_512_2048cnn_2xhighway_weights_10052019.hdf5")
)
elmo_options_file_path = load_abs_path(
os.path.join(os.path.dirname(__file__), '..', "resource", "embedding",
"elmo_model",
"elmo_2x4096_512_2048cnn_2xhighway_options.json"))
sentence4 = [
"i", "really", "enjoy", "Ashley", "and", "Ami", "salon", "she", "do",
"a", "great", "job", "be", "friendly", "and", "professional", "I",
"usually", "get", "my", "hair", "do", "when", "i", "go", "to", "to",
"MI", "because", "of", "the", "quality", "of", "the", "highlight",
"and", "the", "price", "be", "very", "affordable", "the", "highlight",
"fantastic", "thank", "Ashley", "i", "highly", "recommend", "you",
"and", "ill", "be", "back"
]
fine_tuned_elmo = ElmoEmbedder(options_file=elmo_options_file_path,
weight_file=elmo_credbank_model_path)
sentence_vectors = fine_tuned_elmo.embed_sentence(sentence4)
avg_all_layer_sent_embedding = np.mean(sentence_vectors,
axis=0,
dtype='float32')
print("test with self-attentive model: ")
self_attention_elmo_input = torch.stack(
[torch.as_tensor(avg_all_layer_sent_embedding)]).permute(1, 0, 2)
# ELMo output.size() = (batch_size, num_seq, 2*hidden_size)
self_attention_elmo_input = self_attention_elmo_input.permute(1, 0, 2)
print(
"self_attention_elmo_input shape (batch_size, num_seq, 2*hidden_size) : ",
self_attention_elmo_input.shape)
self_attention_model = StructuredSelfAttention(1024)
# print(self_attention_elmo_input)
concatenated_context_embeddings, attn_weight_matrix = self_attention_model.forward(
self_attention_elmo_input, if_concat=True)
print(
"self attention weights (annotation A) of ELMo embedding shape (batch_size, r, num_seq): ",
attn_weight_matrix.shape)
print("attn_weight_matrix: ", attn_weight_matrix)
# assert attn_weight_matrix.shape == torch.Size([22, 30, 1])
# fc_input_tesnor = hidden_matrix.view(-1, hidden_matrix.size()[1]*hidden_matrix.size()[2])
print(
" concatenate the hidden_matrix (b4 feeding into FC and softmax) shape:",
concatenated_context_embeddings.shape)
avg_context_embeddings, attn_weight_matrix = self_attention_model.forward(
self_attention_elmo_input, if_concat=False)
print(
" averaged the hidden_matrix (b4 feeding into FC and softmax) shape:",
avg_context_embeddings.shape)