Logistic-Regression.py

@@ -1,94 +0,0 @@
-import pandas as pd
-import numpy as np
-import torch
-import gensim.downloader as gensim
-from nltk.tokenize import word_tokenize
-
-x_train = pd.read_table('train/in.tsv', sep='\t', header = None, error_bad_lines = False, quoting = 3)
-y_train = pd.read_table('train/expected.tsv', sep='\t', header = None, quoting = 3)
-y_train = y_train[0]
-x_dev = pd.read_table('dev-0/in.tsv', sep='\t', header = None, quoting = 3)
-x_test = pd.read_table('test-A/in.tsv', sep='\t', header = None, quoting = 3)
-
-x_train = x_train[0].str.lower()
-x_dev = x_dev[0].str.lower()
-x_test = x_test[0].str.lower()
-
-x_train = [word_tokenize(x) for x in x_train]
-x_dev = [word_tokenize(x) for x in x_dev]
-x_test = [word_tokenize(x) for x in x_test]
-
-word2vec = gensim.load('glove-wiki-gigaword-50')
-
-def document_vector(doc):
-    return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(50)], axis=0)
-
-x_train = [document_vector(doc) for doc in x_train]
-x_dev = [document_vector(doc) for doc in x_dev]
-x_test = [document_vector(doc) for doc in x_test]
-
-class NeuralNetworkModel(torch.nn.Module):
-    def __init__(self, features):
-        super(NeuralNetworkModel, self).__init__()
-        self.fc1 = torch.nn.Linear(50, features)
-        self.fc2 = torch.nn.Linear(features, 1)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = torch.relu(x)
-        x = self.fc2(x)
-        x = torch.sigmoid(x)
-        return x
-    
-nn_model = NeuralNetworkModel(100)
-BATCH_SIZE = 5
-criterion = torch.nn.BCELoss()
-optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)
-
-for epoch in range(5):
-    nn_model.train()
-    for i in range(0, y_train.shape[0], BATCH_SIZE):
-        X = x_train[i:i+BATCH_SIZE]
-        X = torch.tensor(X)
-        y = y_train[i:i+BATCH_SIZE]
-        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)
-
-        outputs = nn_model(X.float())
-        loss = criterion(outputs, y)
-
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-y_dev = []
-y_test = []
-
-nn_model.eval()
-
-with torch.no_grad():
-    for i in range(0, len(x_dev), BATCH_SIZE):
-        X = x_dev[i:i+BATCH_SIZE]
-        X = torch.tensor(X)
-        
-        outputs = nn_model(X.float())
-        
-        y = (outputs > 0.5)
-        y_dev.extend(y)
-
-    for i in range(0, len(x_test), BATCH_SIZE):
-        X = x_test[i:i+BATCH_SIZE]
-        X = torch.tensor(X)
-
-        outputs = nn_model(X.float())
-
-        y = (outputs > 0.5)
-        y_test.extend(y)
-
-y_dev = np.asarray(y_dev, dtype=np.int32)
-y_test = np.asarray(y_test, dtype=np.int32)
-
-Y_dev = pd.DataFrame({'label':y_dev})
-Y_test = pd.DataFrame({'label':y_test})
-
-Y_dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False,  header=False)
-Y_test.to_csv(r'test-A/out.tsv', sep='\t', index=False,  header=False)

geval-results.txt

@@ -1,5 +0,0 @@
-Likelihood	0.0000
-Accuracy	0.7289
-F1.0	0.5594
-Precision	0.6587
-Recall	0.4861