From 023903113d16b34bdfe3cadcc132f182011e594d Mon Sep 17 00:00:00 2001 From: adnovac Date: Mon, 30 May 2022 21:08:27 +0200 Subject: [PATCH] lstm --- dev-0/out.tsv | 21038 +++++++++++++++++++++++------------------------ run.py | 171 +- test-A/out.tsv | 14828 ++++++++++++++++----------------- 3 files changed, 18061 insertions(+), 17976 deletions(-) diff --git a/dev-0/out.tsv b/dev-0/out.tsv index 8128086..393b043 100644 --- a/dev-0/out.tsv +++ b/dev-0/out.tsv @@ -1,10519 +1,10519 @@ -the:0.31657204031944275 :0.07531388103961945 a:0.03624628484249115 his:0.01748204231262207 which:0.01714618317782879 this:0.016758952289819717 tho:0.012542805634438992 their:0.011488053016364574 -:0.09239458292722702 own:0.023243697360157967 way:0.008648166432976723 use:0.006440419703722 members:0.006146837491542101 present:0.0053787692449986935 first:0.0053429631516337395 work:0.005020772106945515 -the:0.23492033779621124 :0.09103652834892273 a:0.026309818029403687 tho:0.01566087082028389 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to:0.13412253279637537 in:0.11981891718966754 of:0.041923251555380715 was:0.032462607020398056 be:0.03186915118323151 :0.3 +the:0.2219068208307644 and:0.17114327857718784 to:0.14809779985122795 in:0.13677986599785563 a:0.1275569614818034 of:0.1024471323358617 that:0.06932575870402509 was:0.022742382221273984 :0.3 diff --git a/run.py b/run.py index f3aed99..f9998d7 100644 --- a/run.py +++ b/run.py @@ -20,8 +20,9 @@ import csv import itertools from os.path import exists -vocab_size = 30000 -embed_size = 150 +vocab_size = 15000 +embed_size = 128 +lstm_size = 128 # funkcje pomocnicze def clean(text): @@ -46,17 +47,29 @@ def get_word_lines_from_data(d): yield get_words_from_line(line) class Model(torch.nn.Module): - def __init__(self, vocabulary_size, embedding_size): + def __init__(self, vocabulary_size, embedding_size, lstm_size): super(Model, self).__init__() - self.model = torch.nn.Sequential( - torch.nn.Embedding(vocabulary_size, embedding_size), - torch.nn.Linear(embedding_size, vocabulary_size), - torch.nn.Softmax() + self.lstm_size = lstm_size + self.embedding_dim = embedding_size + self.num_layers = 3 + + self.embedding = torch.nn.Embedding( + num_embeddings=vocab_size, + embedding_dim=self.embedding_dim, ) + self.lstm = torch.nn.LSTM( + input_size=self.lstm_size, + hidden_size=self.lstm_size, + num_layers=self.num_layers, + dropout=0.2, + ) + self.fc = torch.nn.Linear(self.lstm_size, vocab_size) - def forward(self, x): - return self.model(x) - + def forward(self, x, prev_state = None): + embed = self.embedding(x) + output, state = self.lstm(embed, prev_state) + logits = self.fc(output) + return logits, state class Trigrams(torch.utils.data.IterableDataset): def __init__(self, data, vocabulary_size): @@ -82,37 +95,41 @@ class Trigrams(torch.utils.data.IterableDataset): # ładowanie danych treningowych -train_in = pd.read_csv("gdrive/MyDrive/train/in.tsv.xz", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=300000)[[6, 7]] -train_expected = pd.read_csv("gdrive/MyDrive/train/expected.tsv", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=300000) +train_in = pd.read_csv("gdrive/MyDrive/train/in.tsv.xz", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=20000)[[6, 7]] +train_expected = pd.read_csv("gdrive/MyDrive/train/expected.tsv", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=20000) train_data = pd.concat([train_in, train_expected], axis=1) train_data = train_data[6] + train_data[0] + train_data[7] train_data = train_data.apply(clean) train_dataset = Trigrams(train_data, vocab_size) +train_dataset_rev = Trigrams(train_data.iloc[::-1], vocab_size) # trenowanie/wczytywanie modelu device = 'cuda' if torch.cuda.is_available() else 'cpu' -model = Model(vocab_size, embed_size).to(device) +model = Model(vocab_size, embed_size, lstm_size).to(device) print(device) + if(not exists('model1.bin')): data = DataLoader(train_dataset, batch_size=8000) - optimizer = torch.optim.Adam(model.parameters()) - criterion = torch.nn.NLLLoss() + optimizer = torch.optim.Adam(model.parameters(), lr=0.001) + criterion = torch.nn.CrossEntropyLoss() model.train() step = 0 - for i in range(2): + for i in range(1): print(f"EPOCH {i}=========================") for x, y in data: + optimizer.zero_grad() x = x.to(device) y = y.to(device) - optimizer.zero_grad() - ypredicted = model(x) - loss = criterion(torch.log(ypredicted), y) + + y_pred, state_h = model(x) + loss = criterion(y_pred, y) + + loss.backward() + optimizer.step() if step % 100 == 0: print(step, loss) step += 1 - loss.backward() - optimizer.step() torch.save(model.state_dict(), 'model1.bin') else: @@ -122,47 +139,115 @@ else: vocab = train_dataset.vocab -def predict(tokens): - ixs = torch.tensor(vocab.forward(tokens)).to(device) +# trenowanie/wczytywanie modelu +device = 'cuda' if torch.cuda.is_available() else 'cpu' +model_b = Model(vocab_size, embed_size, lstm_size).to(device) +print(device) + +if(not exists('model1_b.bin')): + data_b = DataLoader(train_dataset_rev, batch_size=8000) + optimizer = torch.optim.Adam(model_b.parameters(), lr=0.001) + criterion = torch.nn.CrossEntropyLoss() + + model_b.train() + step = 0 + for i in range(1): + print(f"EPOCH {i}=========================") + for x, y in data: + optimizer.zero_grad() + x = x.to(device) + y = y.to(device) + + y_pred, state_h = model_b(x) + loss = criterion(y_pred, y) + + loss.backward() + optimizer.step() + if step % 100 == 0: + print(step, loss) + step += 1 + + torch.save(model_b.state_dict(), 'model1_b.bin') +else: + print("Loading model1") + model_b.load_state_dict(torch.load('model1_b.bin')) + +import numpy as np + +def predict(tokens_left, tokens_right): + ixs = torch.tensor(vocab.forward(tokens_left)).to(device) + ixs_r = torch.tensor(vocab.forward(tokens_right)).to(device) + out = model(ixs) + out_b = model_b(ixs_r) + top = torch.topk(out[0], 8) - top_indices = top.indices.tolist() - top_probs = top.values.tolist() - top_words = vocab.lookup_tokens(top_indices) + top_b = torch.topk(out_b[0], 8) + top_indices = top.indices.tolist()[0] + top_probs = top.values.tolist()[0] + top_indices_b = top_b.indices.tolist()[0] + top_probs_b = top_b.values.tolist()[0] + + + raw_result = [] + for ind in set(top_indices + top_indices_b): + prob = 0 + if(ind in top_indices): + prob += top_probs[top_indices.index(ind)] + if(ind in top_indices_b): + prob += top_probs_b[top_indices_b.index(ind)] + raw_result += [[vocab.lookup_token(ind), prob]] + raw_result = list(filter(lambda x: x[0] != "", raw_result)) + raw_result = sorted(raw_result, key=lambda x: -x[1])[:8] + + words = [x[0] for x in raw_result] + probs = [x[1] for x in raw_result] + + probs_x = np.exp(probs)/sum(np.exp(probs)) result = "" - for word, prob in list(zip(top_words, top_probs)): - result += f"{word}:{prob} " - # result += f':0.01' + for word, prob in list(zip(words,probs_x)): + result += f"{word}:{prob} " + result += ":0.3" + result = result.rstrip() return result from nltk import word_tokenize def predict_file(result_path, data): with open(result_path, "w+", encoding="UTF-8") as f: - for row in data: + for index, row in data.iterrows(): result = {} before = None - for before in get_words_from_line(clean(str(row)), False): + after = None + for after in get_words_from_line(clean(str(row[7])), False): + after = [after] + break + for before in get_words_from_line(clean(str(row[6])), False): pass before = [before] - print(before) - if(len(before) < 1): - result = "a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1" + if(len(before) < 1 and len(after) < 1): + result = "a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1" else: - result = predict(before) + result = predict(before, after) result = result.strip() - f.write(result + "\n") print(result) + f.write(result + "\n") -dev_data = pd.read_csv("gdrive/MyDrive/dev-0/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE)[6] -dev_data = dev_data.apply(clean) +dev_data = pd.read_csv("gdrive/MyDrive/dev-0/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE) +dev_data[6] = dev_data[6].apply(clean) +dev_data[7] = dev_data[7].apply(clean) + predict_file("gdrive/MyDrive/dev-0/out.tsv", dev_data) -test_data = pd.read_csv("gdrive/MyDrive/test-A/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE)[6] -test_data = test_data.apply(clean) +test_data = pd.read_csv("gdrive/MyDrive/test-A/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE) +test_data[6] = test_data[6].apply(clean) +test_data[7] = test_data[7].apply(clean) predict_file("gdrive/MyDrive/test-A/out.tsv", test_data) -!cp -r gdrive/MyDrive/dev-0 dev-0 -!./geval -t dev-0 --metric PerplexityHashed +# !wget https://gonito.net/get/bin/geval +# !chmod 777 geval -!rm -r dev-0 \ No newline at end of file +!rm -r dev-0 + +!cp -r gdrive/MyDrive/dev-0 dev-0 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