07_neural fix
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@ -15,7 +15,7 @@ Perplexity hashed by
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- branch: master - Perplexity hashed on `dev-0`: 555.75
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<br><br>
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2. Neuronowy model językowy (zadanie 7)
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- branch: 07_neural - Perplexity hashed on `dev-0`: xxx
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- branch: 07_neural - Perplexity hashed on `dev-0`: 588.67
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<br><br>
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3. Model neuronowy rekurencyjny (zadanie 9)
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- branch: 09_neural - Perplexity hashed on `dev-0`: xxx
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21038
dev-0/out.tsv
21038
dev-0/out.tsv
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Load Diff
591
main.ipynb
591
main.ipynb
File diff suppressed because one or more lines are too long
109
run.py
109
run.py
@ -9,6 +9,7 @@ import math
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from tqdm import tqdm
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directory = "train/in.tsv.xz"
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directory_expected = "train/expected.tsv"
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directory_dev_0 = "dev-0/in.tsv.xz"
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directory_test_A = "test-A/in.tsv.xz"
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@ -17,64 +18,92 @@ directory_test_A = "test-A/in.tsv.xz"
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n = 3
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device = torch.device("cuda")
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batch_size = 512
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learning_rate = 0.004
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epochs = 1
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embedding_size = 64
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batch_size = 1024
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learning_rate = 0.001
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epochs = 15
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embedding_size = 128
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# --------------------- DATASET ---------------------
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dataframeList = pd.read_csv(directory, sep='\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE, chunksize=10000)
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expectedList = pd.read_csv(directory, sep='\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, chunksize=10000)
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expectedList = pd.read_csv(directory_expected, sep='\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, chunksize=10000)
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DATASET = ""
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count = n - 1
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n_gram = []
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for number, (dataframe, expected) in enumerate(zip(dataframeList, expectedList)):
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dataframe = dataframe.reset_index()
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dataframe = dataframe.replace(r'\\r|\\n|\n|\\t', ' ', regex=True)
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left_text = dataframe['LeftContext'].to_list()
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right_text = dataframe['RightContext'].to_list()
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word = expected['Word'].to_list()
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expected['Word'] = expected['Word'].apply(lambda x: [str(x).strip()])
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word = expected['Word']
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lines = zip(left_text, word, right_text)
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lines = list(map(lambda l: " ".join(l), lines))
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DATASET = DATASET + " ".join(lines)
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# ------------------------------ LEFT ------------------------------
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# dataframe['LeftContext'] = dataframe['LeftContext'].apply(lambda x: re.split(r"\s+", x.strip())[-count:])
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# left_text = dataframe['LeftContext']
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if(number == 15):
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break
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# lines = list(zip(left_text, word))
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# lines = list(map(lambda l: l[0] + l[1], lines))
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FINAL_DATASET = re.split(r"\s+", DATASET)
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print(FINAL_DATASET[:100])
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# ------------------------------ MIDDLE ------------------------------
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dataframe['LeftContext'] = dataframe['LeftContext'].apply(lambda x: re.split(r"\s+", x.strip())[-math.floor(n/2):])
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left_text = dataframe['LeftContext']
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dataframe['RightContext'] = dataframe['RightContext'].apply(lambda x: re.split(r"\s+", x.strip())[:math.floor(n/2)])
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right_text = dataframe['RightContext']
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lines = list(zip(left_text, word, right_text))
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lines = list(map(lambda l: l[0] + l[1] + l[2], lines))
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# ------------------------------ END ------------------------------
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n_gram.extend(lines)
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print(n_gram[:100])
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FINAL_DATASET = n_gram
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# --------------------- TOKENIZE ---------------------
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FINAL_DATASET_TOKENIZED = []
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tokenize_dict = bidict({})
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token = 1
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for i, word in enumerate(FINAL_DATASET):
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if(word in tokenize_dict):
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FINAL_DATASET_TOKENIZED.append(tokenize_dict[word])
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else:
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tokenize_dict[word] = token
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FINAL_DATASET_TOKENIZED.append(token)
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token = token + 1
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for i, n_words in enumerate(FINAL_DATASET):
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n_gram = []
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for j in range(n):
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if(n_words[j] in tokenize_dict):
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n_gram.append(tokenize_dict[n_words[j]])
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else:
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tokenize_dict[n_words[j]] = token
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n_gram.append(token)
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token = token + 1
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FINAL_DATASET_TOKENIZED.append(n_gram)
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# --------------------- N-GRAM & TENSORS ---------------------
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ngram_list = list(nltk.ngrams(FINAL_DATASET_TOKENIZED, n=n))
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# ngram_list = list(nltk.ngrams(FINAL_DATASET_TOKENIZED, n=n))
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ngram_list = FINAL_DATASET_TOKENIZED
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np.random.shuffle(ngram_list)
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tensor_ngram = torch.tensor(ngram_list, device=device)
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# ------------------------------ MIDDLE ------------------------------
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X = torch.cat((tensor_ngram[:, :math.floor(n/2)], tensor_ngram[:, math.ceil(n/2):]), dim = 1).to(device)
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Y = tensor_ngram[:, math.floor(n/2)].reshape(-1, 1).to(device)
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# ------------------------------ LEFT ------------------------------
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# X = tensor_ngram[:, :count].to(device)
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# Y = tensor_ngram[:, count].reshape(-1, 1).to(device)
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# ------------------------------ END ------------------------------
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X_split = torch.split(X, batch_size)
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Y_split = torch.split(Y, batch_size)
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# vocab_size = len(tokenize_dict) + 1
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vocab_size = 20000
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vocab_size = len(tokenize_dict) + 1
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# vocab_size = 50000
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# --------------------- MODEL N-GRAM ---------------------
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@ -95,14 +124,15 @@ class Model(torch.nn.Module):
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def forward(self, inputs):
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out = self.embedding(inputs)
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out = out.view(inputs.size(0), -1)
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# out = torch.relu(out)
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out = torch.softmax(out, dim=1)
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out = self.linear(out)
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out = torch.softmax(out, dim = 1)
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return out
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def train(self, input, output) -> None:
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criterion = torch.nn.CrossEntropyLoss()
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# optimizer = torch.optim.Adam(self.parameters(), lr = learning_rate)
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optimizer = torch.optim.Adam(self.parameters())
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optimizer = torch.optim.Adam(self.parameters(), lr = learning_rate)
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# optimizer = torch.optim.Adam(self.parameters())
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batch_list = list(zip(input, output))
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@ -121,8 +151,13 @@ class Model(torch.nn.Module):
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print("EPOCH: ", epoch, "LOSS: ", total_loss)
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def predict(self, text_beggining:list, text_ending:list) -> list:
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# ------------------------------ MIDDLE ------------------------------
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text_beggining = text_beggining[-math.floor(n/2):]
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text_ending = text_ending[:math.floor(n/2)]
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# ------------------------------ LEFT ------------------------------
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# text_ending = []
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# text_beggining = text_beggining[-count:]
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# ------------------------------ END ------------------------------
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beginning = []
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for word in text_beggining:
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@ -141,7 +176,7 @@ class Model(torch.nn.Module):
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tensor_context = torch.tensor([beginning + ending]).to(device)
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with torch.no_grad():
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result = self(tensor_context)
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result_pred, result_tokens = torch.topk(result, 10)
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result_pred, result_tokens = torch.topk(result, 20)
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words = list(zip(result_tokens[0], result_pred[0]))
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words = [(token_to_word(token), round(float(score), 2)) for token, score in words]
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return words
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@ -150,9 +185,7 @@ class Model(torch.nn.Module):
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model = Model()
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model.to(device)
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model.train(X_split[:2000], Y_split[:2000])
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# 39607
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# model.train(X_split, Y_split)
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model.train(X_split, Y_split)
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# --------------------- PREDICTION ---------------------
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@ -160,14 +193,14 @@ def convert_predictions(line):
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sum_predictions = np.sum([pred[1] for pred in line])
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result = ""
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all_pred = 0
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for word, pred in line:
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for word, pred in line[:10]:
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new_pred = math.floor(pred / sum_predictions * 100) / 100
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if(new_pred == 1.0):
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new_pred = 0.99
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elif(new_pred == 0.0):
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continue
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all_pred = all_pred + new_pred
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result = result + word + ":" + str(new_pred) + " "
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result = result + str(word) + ":" + str(new_pred) + " "
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if(round(all_pred, 2) < 1):
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result = result + ":" + str(round(1 - all_pred, 2))
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else:
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@ -183,8 +216,8 @@ left_text = dataframe['LeftContext'].apply(lambda l: re.split(r"\s+", l)).to_lis
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right_text = dataframe['RightContext'].apply(lambda l: re.split(r"\s+", l)).to_list()
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lines = zip(left_text, right_text)
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lines = list(map(lambda l: model.generate_text(l[0], l[1], False), tqdm(lines)))
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print(lines[:100])
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lines = list(map(lambda l: model.predict(l[0], l[1]), tqdm(lines)))
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print(lines[:40])
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with open("dev-0/out.tsv", "w", encoding="UTF-8") as file:
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result = "\n".join(list(map(lambda l: convert_predictions(l), tqdm(lines))))
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@ -200,8 +233,8 @@ left_text = dataframe['LeftContext'].apply(lambda l: re.split(r"\s+", l)).to_lis
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right_text = dataframe['RightContext'].apply(lambda l: re.split(r"\s+", l)).to_list()
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lines = zip(left_text, right_text)
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lines = list(map(lambda l: model.generate_text(l[0], l[1], False), tqdm(lines)))
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print(lines[:100])
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lines = list(map(lambda l: model.predict(l[0], l[1]), tqdm(lines)))
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print(lines[:40])
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with open("test-A/out.tsv", "w", encoding="UTF-8") as file:
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result = "\n".join(list(map(lambda l: convert_predictions(l), tqdm(lines))))
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14828
test-A/out.tsv
14828
test-A/out.tsv
File diff suppressed because it is too large
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