diff --git a/pages/students/2016/darius_lindvai/dp2021/README.md b/pages/students/2016/darius_lindvai/dp2021/README.md index 4d3fd124c0..431555f066 100644 --- a/pages/students/2016/darius_lindvai/dp2021/README.md +++ b/pages/students/2016/darius_lindvai/dp2021/README.md @@ -1,3 +1,9 @@ +## Update 05.06.2020 +- pridaný čas začiatku a čas ukončenia trénovania, aby bolo možné určit, ako dlho trénovanie trvalo +- upravený skript na úpravu textu do vhodnej podoby (skombinoval som môj vlastný skript s jedným voľne dostupným na internete, aby bola úprava textu presnejšia) +- pridaný tag na identifikáciu čísel v texte ("N"), čo by teoreticky mohlo zvýšiť presnosť modelu +- vyriešený výpočet precision, recall a f-score (problém som vyriešil tak, že som najprv zo skutočných hodnôt urobil tensor, ktorý som následne konvertoval na numpy pole) + ## Update 05.05.2020 - upravený skript "punc.py" tak, že model načítava dáta zo súboru/ov - vytvorený skript "text.py", ktorý upraví dáta do vhodnej podoby (5 krokov) diff --git a/pages/students/2016/darius_lindvai/dp2021/tags.py b/pages/students/2016/darius_lindvai/dp2021/convert_tags.py similarity index 85% rename from pages/students/2016/darius_lindvai/dp2021/tags.py rename to pages/students/2016/darius_lindvai/dp2021/convert_tags.py index d6a866de58..a1e0ac054d 100644 --- a/pages/students/2016/darius_lindvai/dp2021/tags.py +++ b/pages/students/2016/darius_lindvai/dp2021/convert_tags.py @@ -1,5 +1,4 @@ import os -import re if os.path.exists('tags.txt'): os.remove('tags.txt') @@ -11,15 +10,15 @@ with open('text.txt', 'r') as input_file: if (word == '.PER'): word = word.replace(word, 'P') output_file.write(word + ' ') - elif (word == ',COM'): word = word.replace(word, 'C') output_file.write(word + ' ') - elif(word == '?QUE'): word = word.replace(word, 'Q') output_file.write(word + ' ') - + elif(word == ''): + word = word.replace(word, 'N') + output_file.write(word + ' ') else: word = word.replace(word, 'S') output_file.write(word + ' ') diff --git a/pages/students/2016/darius_lindvai/dp2021/prepare_text.py b/pages/students/2016/darius_lindvai/dp2021/prepare_text.py new file mode 100644 index 0000000000..b7116572ac --- /dev/null +++ b/pages/students/2016/darius_lindvai/dp2021/prepare_text.py @@ -0,0 +1,73 @@ +from __future__ import division, print_function +from nltk.tokenize import word_tokenize + +import nltk +import os +from io import open +import re +import sys + +nltk.download('punkt') + +NUM = '' + +PUNCTS = {".": ".PER", ",": ".COM", "?": "?QUE", "!": ".PER", ":": ",COM", ";": ".PER", "-": ",COM"} + +forbidden_symbols = re.compile(r"[\[\]\(\)\/\\\>\<\=\+\_\*]") +numbers = re.compile(r"\d") +multiple_punct = re.compile(r'([\.\?\!\,\:\;\-])(?:[\.\?\!\,\:\;\-]){1,}') + +is_number = lambda x: len(numbers.sub("", x)) / len(x) < 0.6 + +def untokenize(line): + return line.replace(" '", "'").replace(" n't", "n't").replace("can not", "cannot") + +def skip(line): + + if line.strip() == '': + return True + + last_symbol = line[-1] + if not last_symbol in PUNCTS: + return True + + if forbidden_symbols.search(line) is not None: + return True + + return False + +def process_line(line): + + tokens = word_tokenize(line) + output_tokens = [] + + for token in tokens: + + if token in PUNCTS: + output_tokens.append(PUNCTS[token]) + elif is_number(token): + output_tokens.append(NUM) + else: + output_tokens.append(token.lower()) + + return untokenize(" ".join(output_tokens) + " ") + +skipped = 0 + +with open(sys.argv[2], 'w', encoding='utf-8') as out_txt: + with open(sys.argv[1], 'r', encoding='utf-8') as text: + + for line in text: + + line = line.replace("\"", "").strip() + line = multiple_punct.sub(r"\g<1>", line) + + if skip(line): + skipped += 1 + continue + + line = process_line(line) + + out_txt.write(line) + +print("Skipped %d lines" % skipped) diff --git a/pages/students/2016/darius_lindvai/dp2021/punc.py b/pages/students/2016/darius_lindvai/dp2021/punc.py index 6f80a43559..8580aa3373 100644 --- a/pages/students/2016/darius_lindvai/dp2021/punc.py +++ b/pages/students/2016/darius_lindvai/dp2021/punc.py @@ -1,14 +1,13 @@ +import numpy as np import torch import torch.autograd as autograd import torch.nn as nn import torch.optim as optim +from sklearn import metrics +from datetime import datetime torch.manual_seed(1) - - - - def argmax(vec): # return the argmax as a python int _, idx = torch.max(vec, 1) @@ -27,10 +26,6 @@ def log_sum_exp(vec): return max_score + \ torch.log(torch.sum(torch.exp(vec - max_score_broadcast))) - - - - class BiLSTM_CRF(nn.Module): def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim): @@ -65,7 +60,7 @@ class BiLSTM_CRF(nn.Module): torch.randn(2, 1, self.hidden_dim // 2)) def _forward_alg(self, feats): - # Forward algorithm to compute the partition function + # Do the forward algorithm to compute the partition function init_alphas = torch.full((1, self.tagset_size), -10000.) # START_TAG has all of the score. init_alphas[0][self.tag_to_ix[START_TAG]] = 0. @@ -77,13 +72,18 @@ class BiLSTM_CRF(nn.Module): for feat in feats: alphas_t = [] # The forward tensors at this timestep for next_tag in range(self.tagset_size): - # broadcast the emission score: it is the same regardless of the previous tag - emit_score = feat[next_tag].view(1, -1).expand(1, self.tagset_size) - # the ith entry of trans_score is the score of transitioning to next_tag from i + # broadcast the emission score: it is the same regardless of + # the previous tag + emit_score = feat[next_tag].view( + 1, -1).expand(1, self.tagset_size) + # the ith entry of trans_score is the score of transitioning to + # next_tag from i trans_score = self.transitions[next_tag].view(1, -1) - # The ith entry of next_tag_var is the value for the edge (i -> next_tag) before we do log-sum-exp + # The ith entry of next_tag_var is the value for the + # edge (i -> next_tag) before we do log-sum-exp next_tag_var = forward_var + trans_score + emit_score - # The forward variable for this tag is log-sum-exp of all the scores. + # The forward variable for this tag is log-sum-exp of all the + # scores. alphas_t.append(log_sum_exp(next_tag_var).view(1)) forward_var = torch.cat(alphas_t).view(1, -1) terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]] @@ -158,7 +158,7 @@ class BiLSTM_CRF(nn.Module): gold_score = self._score_sentence(feats, tags) return forward_score - gold_score - def forward(self, sentence): + def forward(self, sentence): # dont confuse this with _forward_alg above. # Get the emission scores from the BiLSTM lstm_feats = self._get_lstm_features(sentence) @@ -166,25 +166,12 @@ class BiLSTM_CRF(nn.Module): score, tag_seq = self._viterbi_decode(lstm_feats) return score, tag_seq - - - - START_TAG = "" STOP_TAG = "" EMBEDDING_DIM = 5 HIDDEN_DIM = 4 -''' -training_data = [( - "hovorí sa ,COM že ľudstvo postihuje nová epidémia ,COM šíriaca sa závratnou rýchlosťou .PER preto je dôležité vedieť čo to je ,COM ako jej predísť alebo ako ju odstrániť .PER".split(), - "S S C S S S S S C S S S S P S S S S S S S C S S S S S S S P".split() -), ( - "nárast obezity je spôsobený najmä spôsobom života .PER tuky zlepšujú chuť do jedla a dávajú lepší pocit sýtosti ,COM uvedomte si však ,COM že všetky tuky sa Vám ukladajú ,COM pokiaľ ich nespálite .PER".split(), - "S S S S S S S P S S S S S S S S S S C S S S C S S S S S S C S S S P".split() -)] -''' - +# Make up some training data with open('/home/dlindvai/work/text.txt', 'r') as text2: with open('/home/dlindvai/work/tags.txt', 'r') as tags2: text1 = text2.read().splitlines() @@ -200,38 +187,60 @@ training_data = [( text.split() , tags.split() )] word_to_ix = {} for sentence, tags in training_data: - for word in sentence: - if word not in word_to_ix: - word_to_ix[word] = len(word_to_ix) + for word in sentence: + if word not in word_to_ix: + word_to_ix[word] = len(word_to_ix) -tag_to_ix = {"S": 0, "C": 1, "P": 2, "Q": 3, START_TAG: 4, STOP_TAG: 5} +tag_to_ix = {"S": 0, "P": 1, "C": 2, "Q": 3, "N": 4, START_TAG: 5, STOP_TAG: 6} model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM) optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4) + +# Check predictions before training with torch.no_grad(): - precheck_sent = prepare_sequence(training_data[0][0], word_to_ix) - precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long) - print("Predicted output before training: ", model(precheck_sent)) + precheck_sent = prepare_sequence(training_data[0][0], word_to_ix) + precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long) + #print(model(precheck_sent)) -for epoch in range(30): # normally you would NOT do 300 epochs, but this is small dataset - for sentence, tags in training_data: - # Step 1. Remember that Pytorch accumulates gradients. - # We need to clear them out before each instance - model.zero_grad() - # Step 2. Get our inputs ready for the network, that is, turn them into Tensors of word indices. - sentence_in = prepare_sequence(sentence, word_to_ix) - targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long) +# Print start time +start = datetime.now() +start_time = start.strftime("%H:%M:%S") +print("Start time = ", start_time) - # Step 3. Run our forward pass. - loss = model.neg_log_likelihood(sentence_in, targets) +for epoch in range(50): + for sentence, tags in training_data: + # Step 1. Remember that Pytorch accumulates gradients. We need to clear them out before each instance + model.zero_grad() - # Step 4. Compute the loss, gradients, and update the parameters by calling optimizer.step() - loss.backward() - optimizer.step() + # Step 2. Get our inputs ready for the network, that is, turn them into Tensors of word indices. + sentence_in = prepare_sequence(sentence, word_to_ix) + targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long) + # Step 3. Run our forward pass. + loss = model.neg_log_likelihood(sentence_in, targets) + + # Step 4. Compute the loss, gradients, and update the parameters by calling optimizer.step() + loss.backward() + optimizer.step() + + +# Check predictions after training with torch.no_grad(): - precheck_sent = prepare_sequence(training_data[0][0], word_to_ix) - print("Predicted output after training: ", model(precheck_sent)) + precheck_sent = prepare_sequence(training_data[0][0], word_to_ix) + #print(model(precheck_sent)) + +# Error calculator +var = model(precheck_sent) +y_true = np.array(targets) +y_pred = np.array(var[1]) + +print(metrics.confusion_matrix(y_true, y_pred)) +print(metrics.classification_report(y_true, y_pred, digits=3)) + +# Print finish time +finish = datetime.now() +finish_time = finish.strftime("%H:%M:%S") +print("Finish time = ", finish_time) diff --git a/pages/students/2016/darius_lindvai/dp2021/text.py b/pages/students/2016/darius_lindvai/dp2021/text.py deleted file mode 100644 index c645bacb39..0000000000 --- a/pages/students/2016/darius_lindvai/dp2021/text.py +++ /dev/null @@ -1,14 +0,0 @@ -import re -import os - -if os.path.exists('text.txt'): - os.remove('text.txt') - -with open('/home/dlindvai/work/train.txt', 'r') as input_file: - with open('/home/dlindvai/work/text.txt', 'a') as output_file: - for line in input_file: - line = line.replace('\n', '') - line = re.sub(r"([\w/'+$\s-]+|[^\w/'+$\s-]+)\s*", r"\1 ", line) - line = line.lower() - line = line.replace('.','.PER').replace(',',',COM').replace('?','?QUE') - output_file.write(line)