run.py

@@ -1,105 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-from torchvision import transforms
-import pickle
-import numpy as np
-import pandas as pd
-from word2vec import Word2Vec
-
-
-class FFN(nn.Module):
-
-    def __init__(self, input_dim, output_dim, hidden1_size, hidden2_size, lr, epochs, batch_size):
-        super(FFN, self).__init__()
-        self.path = 'model1.pickle'
-        self.lr = lr
-        self.epochs = epochs
-        self.output_dim = output_dim
-        self.word2vec = Word2Vec()
-        self.word2vec.load()
-        self.batch_size = batch_size
-        self.input_dim = input_dim
-        self.fc1 = nn.Linear(batch_size, hidden1_size)
-        self.fc2 = nn.Linear(hidden1_size, hidden2_size)
-        self.fc3 = nn.Linear(hidden2_size, hidden2_size)
-        self.fc4 = nn.Linear(hidden2_size, hidden2_size)
-        self.fc5 = nn.Linear(hidden2_size, batch_size)
-
-    def forward(self, data):
-        data = F.relu(self.fc1(data))
-        data = F.relu(self.fc2(data))
-        data = F.relu(self.fc3(data))
-        data = F.relu(self.fc4(data))
-        data = F.sigmoid(self.fc5(data))
-        return data
-
-    def serialize(self):
-        with open(self.path, 'wb') as file:
-            pickle.dump(self, file)
-
-    def load(self):
-        with open(self.path, 'rb') as file:
-            self = pickle.load(file)
-
-    def batch(self, iterable, n=1):
-        l = len(iterable)
-        for ndx in range(0, l, n):
-            yield iterable[ndx:min(ndx + n, l)]
-
-    """
-    data is a tuple of embedding vector and a label of 0/1
-    """
-
-    def train(self, data, expected):
-        self.zero_grad()
-        criterion = torch.nn.BCELoss()
-        optimizer = optim.Adam(self.parameters(), lr=self.lr)
-        batch_size = self.batch_size
-        num_of_classes = self.output_dim
-        for epoch in range(self.epochs):
-            epoch_loss = 0.0
-            idx = 0
-            for i in range(0, int(len(data) / batch_size) * batch_size, batch_size):
-                inputs = data[i:i + batch_size]
-                labels = expected[i:i + batch_size]
-                optimizer.zero_grad()
-                outputs = self.forward(torch.tensor(self.word2vec.list_of_sentences2vec(inputs)))
-                target = torch.tensor(labels.values).double()
-                loss = criterion(outputs.view(batch_size), target.view(-1, ))
-                loss.backward()
-                optimizer.step()
-
-                epoch_loss += loss.item()
-                if (idx % 1000 == 0):
-                    print('epoch: {}, idx: {}, loss: {}'.format(epoch, idx, epoch_loss / 1000))
-                    epoch_loss = 0
-                idx += 1
-        self.serialize()
-
-    def test(self, data, expected, path):
-        correct = 0
-        incorrect = 0
-        total = 0
-        predictions = []
-        batch_size = self.batch_size
-        for i in range(0, int(len(data) / batch_size) * batch_size, batch_size):
-            inputs = data[i:i + batch_size]
-            labels = expected[i:i + batch_size]
-            predicted = self.forward(torch.tensor(self.word2vec.list_of_sentences2vec(inputs)))
-            score = [1 if x > 0.5 else 0 for x in predicted]
-
-            for x, y in zip(score, labels):
-                if (x == y):
-                    correct += 1
-                else:
-                    incorrect += 1
-            predictions.append(score)
-
-        print(correct)
-        print(incorrect)
-        print(correct / (incorrect + correct))
-        df = pd.DataFrame(np.asarray(predictions).reshape(int(len(data) / batch_size) * batch_size))
-        df.reset_index(drop=True, inplace=True)
-        df.to_csv(path, sep="\t", index=False)

run_bayes.py

@@ -1,31 +0,0 @@
-import lzma
-import nltk
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-with lzma.open("train/in.tsv.xz", "rt", encoding="utf-8") as train_file:
-    in_train = [x.strip().lower() for x in train_file.readlines()]
-
-with open("train/expected.tsv", "r", encoding="utf-8") as train_file:
-    out_train = [int(x.strip()) for x in train_file.readlines()]
-
-with lzma.open("dev-0/in.tsv.xz", "rt", encoding="utf-8") as dev_file:
-    in_dev = [x.strip().lower() for x in dev_file.readlines()]
-
-with lzma.open("test-A/in.tsv.xz", "rt", encoding="utf-8") as test_file:
-    in_test = [x.strip().lower() for x in test_file.readlines()]
-
-tfidf_vectorizer=TfidfVectorizer()
-IN_train = tfidf_vectorizer.fit_transform(in_train)
-classifier = MultinomialNB()
-y_pred = classifier.fit(IN_train, out_train)
-
-y_prediction = y_pred.predict(tfidf_vectorizer.transform(in_test))
-with open("test-A/out.tsv", "w", encoding="utf-8") as test_out_file:
-    for single_pred in y_prediction:
-        test_out_file.writelines(f"{str(single_pred)}\n")
-
-pred_dev = y_pred.predict(tfidf_vectorizer.transform(in_test))
-with open("dev-0/out.tsv", "w", encoding="utf-8") as dev_out_file:
-    for single_pred in pred_dev:
-        dev_out_file.writelines(f"{str(single_pred)}\n")