def createDics(self, bin_dataframe,pretrained_embeddings):
# bin_np = bin_dataframe.as_matrix()
bin_np = bin_dataframe.to_numpy()
if pretrained_embeddings==False: # maybe not need this!
self.word_to_ix["<unk>"] = len(self.word_to_ix)
#initialize the event dictionary
self.event_to_ix["non-event"]= len(self.event_to_ix)
self.event_to_ix["event"]= len(self.event_to_ix)
# initialize the tags dictionary
self.tag_to_ix["B-Other"] = len(self.tag_to_ix)
self.tag_to_ix["I-Other"] = len(self.tag_to_ix)
for line in bin_np:
if line[1] != None:
if pretrained_embeddings==True:
continue
else:
for word in utils.strToLst(line[1]):
if word not in self.word_to_ix:
self.word_to_ix[word] = len(self.word_to_ix)
else:
tag = utils.strToLst(line[0])['corrected_tags']
if tag not in self.tag_to_ix:
self.tag_to_ix[tag] = len(self.tag_to_ix)
self.BIOset, self.ECset = utils.getSortedTagsFromBIO(self.tag_to_ix)
self.tag_to_ix = utils.getSegmentationDict(self.BIOset)
self.ec_to_ix = utils.getSegmentationDict(self.ECset)
def __init__(self, file):
docNr = -1
self.head_docs = []
tokens = headIdDoc("")
for i in range(file.shape[0]):
if '#doc' in file[i][0] or i == file.shape[
0] - 1: # append all docs including the last one
if (i == file.shape[0] - 1): # append last line
tokens.append(int(file[i][0]), file[i][1], file[i][2],
utils.strToLst(file[i][3]),
utils.strToLst(file[i][4])) # append lines
if (docNr != -1):
self.head_docs.append(tokens)
docNr += 1
tokens = headIdDoc(file[i][0])
else:
tokens.append(int(file[i][0]), file[i][1], file[i][2],
utils.strToLst(file[i][3]),
utils.strToLst(file[i][4])) # append lines
def __init__(self,fname):
config_file=parsers.read_properties(fname)
#print("\nConfiguration file {} loaded \n".format(fname))
self.config_fname=fname
# load data
self.pretrained_embeddings=utils.strToBool(config_file.getProperty("pretrained_embeddings"))
self.filename_embeddings = config_file.getProperty("filename_embeddings")
#print(os.path.basename(self.filename_embeddings))
name_of_embeddings = ""
self.embeddings_size=int(config_file.getProperty("embeddings_size"))
self.word_to_ix={}
if self.pretrained_embeddings==True:
name_of_embeddings = "_"+os.path.basename(self.filename_embeddings)
if os.path.isfile(self.filename_embeddings+".pkl")==False:
self.wordvectors, self.embeddings_size, self.word_to_ix = utils.readWordvectorsNumpy(self.filename_embeddings, isBinary=True if self.filename_embeddings.endswith(".bin") else False)
joblib.dump(( self.wordvectors, self.embeddings_size, self.word_to_ix), self.filename_embeddings+".pkl")
else:
self.wordvectors, self.embeddings_size, self.word_to_ix = joblib.load(self.filename_embeddings + ".pkl") # loading is faster
self.filename_train=config_file.getProperty("filename_train")
self.filename_dev = config_file.getProperty("filename_dev")
self.filename_test=config_file.getProperty("filename_test")
self.pad_length = int(config_file.getProperty("pad_length"))
'''
train = reader.BinDataset(self.filename_train, isTrain=True, pretrained_embeddings=self.pretrained_embeddings,
word_to_ix=self.word_to_ix,pad_length=self.pad_length)
self.word_to_ix, self.tag_to_ix, self.event_to_ix, self.ec_to_ix = train.getDictionaries()
dev = reader.BinDataset(self.filename_dev,isTrain=False, word_to_ix=self.word_to_ix, tag_to_ix=self.tag_to_ix, event_to_ix=self.event_to_ix,pad_length=self.pad_length)
test = reader.BinDataset(self.filename_test,isTrain=False, word_to_ix=self.word_to_ix, tag_to_ix=self.tag_to_ix, event_to_ix=self.event_to_ix,pad_length=self.pad_length)
'''
if os.path.isfile(self.filename_train +name_of_embeddings+"_"+str(self.pad_length)+ "_tweet.pkl") == False:
train = reader.BinDataset(self.filename_train,isTrain=True,pretrained_embeddings=self.pretrained_embeddings,word_to_ix=self.word_to_ix,pad_length=self.pad_length)
joblib.dump(train, self.filename_train +name_of_embeddings+"_"+str(self.pad_length)+ "_tweet.pkl")
else:
train = joblib.load(self.filename_train+name_of_embeddings+"_"+str(self.pad_length)+ "_tweet.pkl") # loading is faster
self.word_to_ix, self.tag_to_ix, self.event_to_ix, self.ec_to_ix = train.getDictionaries()
if os.path.isfile(self.filename_dev +name_of_embeddings+"_"+str(self.pad_length)+ "_tweet.pkl") == False:
dev = reader.BinDataset(self.filename_dev,isTrain=False, word_to_ix=self.word_to_ix, tag_to_ix=self.tag_to_ix, event_to_ix=self.event_to_ix,pad_length=self.pad_length)
joblib.dump(dev, self.filename_dev +name_of_embeddings+"_"+str(self.pad_length)+ "_tweet.pkl")
else:
dev = joblib.load(self.filename_dev +name_of_embeddings+"_"+str(self.pad_length)+ "_tweet.pkl") # loading is faster
if os.path.isfile(self.filename_test +name_of_embeddings+"_"+str(self.pad_length)+ "_tweet.pkl") == False:
test = reader.BinDataset(self.filename_test,isTrain=False, word_to_ix=self.word_to_ix, tag_to_ix=self.tag_to_ix, event_to_ix=self.event_to_ix,pad_length=self.pad_length)
joblib.dump(test, self.filename_test +name_of_embeddings+"_"+str(self.pad_length)+ "_tweet.pkl")
else:
test = joblib.load(self.filename_test +name_of_embeddings+"_"+str(self.pad_length)+ "_tweet.pkl") # loading is faster
print (train)
self.train_loader = DataLoader(train, batch_size=1, shuffle=False)
self.dev_loader = DataLoader(dev, batch_size=1, shuffle=False)
self.test_loader = DataLoader(test, batch_size=1, shuffle=False)
print ()
#self.dev_id_docs = parsers.readHeadFile( self.filename_dev)
#self.test_id_docs = parsers.readHeadFile(self.filename_test)
# get labels for the whole collection
#dataset_documents = []
#dataset_documents.extend(self.train_id_docs)
#dataset_documents.extend(self.dev_id_docs)
#dataset_documents.extend(self.test_id_docs)
#self.dataset_set_characters = utils.getCharsFromDocuments(dataset_documents)
#self.dataset_set_bio_tags, self.dataset_set_ec_tags = utils.getEntitiesFromDocuments(dataset_documents)
#self.dataset_set_relations = utils.getRelationsFromDocuments(dataset_documents)
#print (len(self.dataset_set_characters))
#print(len(self.dataset_set_bio_tags))
#print((self.dataset_set_characters))
# print((self.dataset_set_bio_tags))
# training
self.nepochs = int(config_file.getProperty("nepochs"))
self.optimizer = config_file.getProperty("optimizer")
#self.activation =config_file.getProperty("activation")
self.learning_rate =float(config_file.getProperty("learning_rate"))
#self.nepoch_no_imprv = int(config_file.getProperty("nepoch_no_imprv"))
self.use_dropout = utils.strToBool(config_file.getProperty("use_dropout"))
self.use_BIO_LSTM = utils.strToBool(config_file.getProperty("use_BIO_LSTM"))
self.ner_loss = config_file.getProperty("ner_loss")
self.ner_classes = config_file.getProperty("ner_classes")
self.bin_features = config_file.getProperty("bin_features").lower()
self.tweet_representation = config_file.getProperty("tweet_representation").lower()
self.non_linearity_bin_features = config_file.getProperty("non_linearity_bin_features").lower()
try:
self.threshold = float(config_file.getProperty("threshold"))
except:
self.threshold=0
# hyperparameters
self.n_filters = int(config_file.getProperty("n_filters"))
self.filter_sizes = utils.strToLst(config_file.getProperty("filter_sizes"))
self.batch_norm = utils.strToBool(config_file.getProperty("batch_norm"))
self.cnn_pool = config_file.getProperty("cnn_pool").lower()
self.dropout_cnn = float(config_file.getProperty("dropout_cnn"))
self.bin_representation = config_file.getProperty("bin_representation").lower()
self.dropout_lstm1_output = float(config_file.getProperty("dropout_lstm1_output"))
self.dropout_embedding = float(config_file.getProperty("dropout_embedding"))
#self.dropout_lstm = float(config_file.getProperty("dropout_lstm"))
self.dropout_lstm2_output = float(config_file.getProperty("dropout_lstm2_output"))
self.dropout_fcl_ner = float(config_file.getProperty("dropout_fcl_ner"))
self.dropout_fcl_rel = float(config_file.getProperty("dropout_fcl_rel"))
#self.hidden_size_lstm =int(config_file.getProperty("hidden_size_lstm"))
self.hidden_dim = int(config_file.getProperty("hidden_dim"))
#self.hidden_size_n2 = config_file.getProperty("hidden_size_n2")
self.num_lstm_layers = int(config_file.getProperty("num_lstm_layers"))
#self.char_embeddings_size = int(config_file.getProperty("char_embeddings_size"))
#self.hidden_size_char = int(config_file.getProperty("hidden_size_char"))
#self.label_embeddings_size = int(config_file.getProperty("label_embeddings_size"))
#self.alpha = float(config_file.getProperty("alpha"))
# evaluation
self.evaluation_method =config_file.getProperty("evaluation_method")
#self.root_node=bool(config_file.getProperty("root_node"))
self.shuffle=False
def preprocess(self, bin_dataframe):
# bin_np = bin_dataframe.as_matrix()
bin_np = bin_dataframe.to_numpy()
docNr = -1
bin_tweets = []
bin_tweet_lengths=[]
bin_tweets_text=[]
previous_match = ""
match = []
for i in range(bin_np.shape[0]):
if bin_np[i][1] == None or i == bin_np.shape[0] - 1: # append all docs including the last one
if (i == bin_np.shape[0] - 1): # append last line
tweet_text = utils.lstToString(utils.strToLst(bin_np[i][1])).split()
tweet, tweet_length = utils.prepare_sequence(
tweet_text, self.word_to_ix,
pad_length=self.pad_length)
bin_tweets.append(tweet)
bin_tweet_lengths.append(tweet_length)
bin_tweets_text.append(tweet_text)
if (docNr != -1):
#bin_tweets = np.asarray(bin_tweets)
try:
tag_id = self.tag_to_ix[target]
if target.startswith("B-") or target.startswith("I-"):
ec_id=self.ec_to_ix[target[2:]]
else:
ec_id=self.ec_to_ix[target]
except:
# print(target)
if target.startswith("B-"):
tag_id = self.tag_to_ix["B-Other"]
elif target.startswith("I-"):
tag_id = self.tag_to_ix["I-Other"]
ec_id = self.ec_to_ix["Other"]
if target=="O":
event_duration_idx = self.event_to_ix["non-event"]
else:
event_duration_idx = self.event_to_ix["event"]
if event_id==-1:
independent_event_idx = self.event_to_ix["non-event"]
else:
independent_event_idx = self.event_to_ix["event"]
#print (len(bin_tweets))
#print (torch.stack(bin_tweets))
match.append([torch.stack(bin_tweets), tag_id,ec_id,event_duration_idx,independent_event_idx,event_type,event_id,bin_tweet_lengths])
#print (utils.getDictionaryKeyByIdx(self.tag_to_ix,tag_id),utils.getDictionaryKeyByIdx(self.ec_to_ix,ec_id),utils.getDictionaryKeyByIdx(self.event_to_ix,event_id))
# match=np.append(match,bin_tokens)
# match['match_bins'].append(bin)
docNr += 1
if i != bin_np.shape[0] - 1:
infoDict = utils.strToLst(bin_np[i][0])
# print('infoDict', infoDict)
if previous_match != infoDict['doc']:
# print (infoDict['doc'])
# match = {'match_bins': np.empty((0)),"match_name": infoDict['doc']}
previous_match = infoDict['doc']
# below two lines should be interchanged i think
match = []
self.matches.append(match)
bin_tweets = []
bin_tweet_lengths=[]
bin_tweets_text=[]
target = infoDict['corrected_tags']
event_type = infoDict['event_type']
event_id = infoDict['event_id']
match_name= infoDict['doc']
# {'bin': infoDict['bin'],'targets': infoDict['corrected_tags'],'tweets':[],'timestamps':[],'tokens':""}
else:
# bin['tweets'].append(strToLst(bin_np[i][1]))
# bin_tokens+=" "+lstToString(strToLst(bin_np[i][1]))
# bin['timestamps'].append(int(bin_np[i][0]))
# print ((lstToString(strToLst(bin_np[i][1])).split()))
#print (bin_tokens)
tweet_text=utils.lstToString(utils.strToLst(bin_np[i][1])).split()
tweet,tweet_length=utils.prepare_sequence(tweet_text, self.word_to_ix,
pad_length=self.pad_length)
bin_tweets.append(tweet)
bin_tweet_lengths.append(tweet_length)
bin_tweets_text.append(tweet_text)
