From 939388fdcae67ab7274de54960fbacba32e36af3 Mon Sep 17 00:00:00 2001 From: SzamanFL Date: Sun, 14 Jun 2020 17:44:28 +0200 Subject: [PATCH] use LSTM --- dev-0/out.tsv | 10544 +++++++++++++++++++++++------------------------ test-A/out.tsv | 10304 ++++++++++++++++++++++----------------------- train.py | 255 +- 3 files changed, 10600 insertions(+), 10503 deletions(-) diff --git a/dev-0/out.tsv b/dev-0/out.tsv index 30667af..ffc11eb 100644 --- a/dev-0/out.tsv +++ b/dev-0/out.tsv @@ -1,5272 +1,5272 @@ -0.079540 -0.562190 -0.797391 -0.316725 -0.078697 -0.394304 -0.000002 -0.526481 -0.405349 -0.312168 -0.000010 -0.403824 -0.036009 -0.881680 -0.006849 -0.594469 -0.054782 -0.683693 -0.133150 -0.893434 -0.303161 -0.519036 -0.999588 -0.462618 -0.906008 -0.000000 -0.887290 -0.451947 -0.285531 -0.409614 -0.000001 -0.955754 -0.057076 -0.561157 -0.000000 -0.081489 -0.411115 -0.024469 -0.673782 -0.102467 -0.647510 -0.144991 -0.006049 -0.681770 -0.000191 -0.000051 -0.760688 -0.092913 -0.376379 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output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5): + super().__init__() + self.output_size = output_size + self.n_layers = n_layers + self.hidden_dim = hidden_dim + + self.embedding = nn.Embedding(vocab_size, embedding_dim) + self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, dropout=drop_prob, batch_first=True) + + self.dropout = nn.Dropout(0.3) + + self.fc = nn.Linear(hidden_dim, output_size) + self.sig = nn.Sigmoid() + + def forward(self, x, hidden): + batch_size = x.size(0) + embeds = self.embedding(x) + lstm_out, hidden = self.lstm(embeds, hidden) + + lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim) + out = self.dropout(lstm_out) + out = self.fc(out) + sig_out = self.sig(out) + sig_out = sig_out.view(batch_size, -1) + sig_out = sig_out[:, -1] + return sig_out, hidden + + def init_hidden(self, batch_size): + weight = next(self.parameters()).data + + if (train_on_gpu): + hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda(), weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda()) + else: + hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_(), weight.new(self.n_layers, batch_size, self.hidden_dim).zero_()) + return hidden + def clear_post(post): post = post.replace('\\n', ' ') @@ -17,94 +65,143 @@ def clear_post(post): post_no_stop = [w for w in post if not w in stop_words] return post_no_stop -# czy słowa musza byc setem? -def create_vocabulary_and_documents(in_file, expected_file): - vocabulary = set() - posts = {} - with open(in_file) as in_f, open(expected_file) as exp_f: - for line, exp in zip(in_f, exp_f): - text, timestap = line.rstrip('\n').split('\t') - post = clear_post(text) - posts[" ".join(post)] = int(exp) - for word in post: - vocabulary.add(word) - with open('data', 'wb') as f: - pickle.dump([vocabulary, posts], f) - print("data created") - return vocabulary, posts +def count_all_words(posts): + joint_posts = ' '.join(posts) + words = joint_posts.split() + count_words = Counter(words) + total_words = len(words) + sorted_words = count_words.most_common(total_words) + return sorted_words, total_words, count_words -def create_mappings(vocabulary): - word_to_index_mapping = {} - index_to_word_mapping = {} - xi = 1 - for word in vocabulary: - word_to_index_mapping[word] = xi - index_to_word_mapping[xi] = word - xi += 1 - return word_to_index_mapping, index_to_word_mapping +def pad_features(posts_int, seq_length): + features = np.zeros((len(posts_int), seq_length), dtype = int) + for i, post in enumerate(posts_int): + post_len = len(post) + if post_len <= seq_length: + zeroes = list(np.zeros(seq_length-post_len)) + new = zeroes+post + + elif post_len > seq_length: + new = post[0:seq_length] + + features[i,:] = np.array(new) + + return features def main(): - if len(sys.argv) != 4: - print("syntax ./train.py model expected_file in_file") + if len(sys.argv) != 3: return - model = str(sys.argv[1]) - expected_file = str(sys.argv[2]) - in_file = str(sys.argv[3]) - try: - with open("data", 'rb') as pos: - pickle_list = pickle.load(pos) - print("data loaded") - vocabulary = pickle_list[0] - posts = pickle_list[1] - except FileNotFoundError: - vocabulary, posts = create_vocabulary_and_documents(in_file, expected_file) - word_to_index_mapping, index_to_word_mapping = create_mappings(vocabulary) + in_file = sys.argv[1] + expected_file = sys.argv[2] - weights = [] - for xi in range(0, len(vocabulary) + 1): - weights.append(random.uniform(-0.01,0.01)) + posts = [] + labels = [] + with open(in_file, 'r') as f: + for line in f: + post = line.split('\t')[0].rstrip().lower() + post = ''.join([c for c in post if c not in punctuation]) + posts.append(post) - learning_rate = 0.000000001 - loss_sum = 0.0 - loss_sum_counter = 0 - lowest_loss_sum_weights = [] - lowest_loss_sum = 10000.0 + with open(expected_file, 'r') as f: + for line in f: + labels.append(int(line)) - print(f"len of vocabulary {len(vocabulary)}") - # mozna ustawić na bardzo bardzo duzo - while loss_sum_counter != 10000: - try: - d, y = random.choice(list(posts.items())) - y_hat = weights[0] - tokens = d.split(' ') - for word in tokens: - # mozna tez cos pomyslec z count aby lepiej dzialalo - #print(f"{d.count(word)} : {word}") - y_hat += weights[word_to_index_mapping[word]] * tokens.count(word) - #print(f"{weights[word_to_index_mapping[word]]} : {word}") + sorted_words, total_words, count_words = count_all_words(posts) + vocab_to_int = {w:i+1 for i, (w,c) in enumerate(sorted_words)} - loss = (y_hat - y)**2 - loss_sum += loss - delta = (y_hat - y) * learning_rate - if loss_sum_counter % 100 == 0: - print(f"{loss_sum_counter} : {loss_sum /1000} : {y_hat} : {delta} : {lowest_loss_sum}") - #loss_sum_counter = 0 - loss_sum = 0 + posts_int = [] + for post in posts: + p = [vocab_to_int[w] for w in post.split()] + posts_int.append(p) - weights[0] -= delta - for word in tokens: - weights[word_to_index_mapping[word]] -= tokens.count(word) * delta + encoded_labels = np.array(labels) - if lowest_loss_sum > loss_sum and loss_sum != 0: - print(f"it happened, new lowest_sum {loss_sum}") - lowest_loss_sum = loss_sum - lowest_loss_sum_weights = weights + posts_len = [len(p) for p in posts_int] + pd.Series(posts_len).hist() + print(pd.Series(posts_len).describe()) + # outliners + posts_int = [posts_int[i] for i, l in enumerate(posts_len) if l>2 ] + encoded_labels = [ encoded_labels[i] for i, l in enumerate(posts_len) if l> 2 ] - loss_sum_counter +=1 - except KeyboardInterrupt: - break - #print(lowest_loss_sum_weights) - with open(model, 'wb') as f: - pickle.dump([weights, lowest_loss_sum_weights, word_to_index_mapping], f) + seq_length = 63 + train_x = pad_features(posts_int, seq_length) + train_y = np.array(encoded_labels) + + train_data = TensorDataset(torch.from_numpy(train_x), torch.from_numpy(train_y)) + batch_size = 50 + + train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size, drop_last = True) + + dataiter = iter(train_loader) + sample_x, sample_y = dataiter.next() + + vocab_size = len(vocab_to_int)+1 + output_size = 1 + embedding_dim = 400 + hidden_dim = 256 + n_layers = 2 + + model = ClassifyLSTM(vocab_size, output_size, embedding_dim, hidden_dim, n_layers) + + lr=0.001 + criterion = nn.BCELoss() + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + epochs = 4 + counter = 0 + print_every = 100 + clip=5 + if(train_on_gpu): + model.cuda() + + model.train() + for e in range(epochs): + h = model.init_hidden(batch_size) + for inputs, labels in train_loader: + counter += 1 + if(train_on_gpu): + inputs, labels = inputs.cuda(), labels.cuda() + h = tuple([each.data for each in h]) + model.zero_grad() + inputs = inputs.type(torch.LongTensor) + output, h = model(inputs, h) + loss = criterion(output.squeeze(), labels.float()) + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), clip) + optimizer.step() + + if counter % print_every == 0: + val_h = model.init_hidden(batch_size) + val_losses = [] + model.eval() + #for inputs, labels in valid_loader: + # val_h = tuple([each.data for each in val_h]) + # if(train_on_gpu): + # inputs, labels = inputs.cuda(), labels.cuda() + # inputs = inputs.type(torch.LongTensor) + # output, val_h = model(inputs, val_h) + # val_loss = criterion(output.squeeze(), labels.float()) + # val_losses.append(val_loss.item()) + #model.train() + print("Epoch: {}/{}...".format(e+1, epochs), + "Step: {}...".format(counter), + "Loss: {:.6f}...".format(loss.item()), + "Val Loss: {:.6f}".format(np.mean(val_losses))) + +# test_losses = [] +# num_correct = 0 +# h = model.init_hidden(batch_size) +# model.eval() +# for inputs, labels in test_loader: +# h = tuple([each.data for each in h]) +# if(train_on_gpu): +# inputs, labels = inputs.cuda(), labels.cuda() +# +# inputs = inputs.type(torch.LongTensor) +# output, h = model(inputs, h) +# test_loss = criterion(output.squeeze(), labels.float()) +# test_losses.append(test_loss.item()) + + + ip.set_trace() main()