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